
#pragma nodebook off
#Use nodebook for better reproducibility https://github.com/uoa-eResearch/nodebook
%reload_ext nodebook.ipython
%nodebook disk phase2


# load libraries
import geopandas as gpd # vector data
import pandas as pd # tabular data, loading CSVs
import numpy as np # numeric data
from util import *
import matplotlib # plotting
from matplotlib_scalebar.scalebar import ScaleBar # scalebar for plot
import matplotlib.pyplot as plt # plotting
from tqdm.auto import tqdm # progress bars
tqdm.pandas()
import json
from shapely.geometry import Point, shape # creating points
import requests # web requests
plt.rcParams['figure.figsize'] = (20, 20)


ls()

Total: 1299.0MB


df = gpd.read_file("input/Modified_Community_Boards_SHP.zip")
df.OBJECTID = df.OBJECTID.astype(int)
df = df.set_index("OBJECTID", drop=True)
df = df.sort_index()
display(df)


df = df.rename(columns={"Local_Area": "Name"})


df["Centroid_lon"] = df.centroid.to_crs(epsg=4326).x
df["Centroid_lat"] = df.centroid.to_crs(epsg=4326).y
df["Area"] = df.area


%%time
zones = gpd.read_file("input/MASTER_UP_BaseZone_SHP.zip")
zones

CPU times: user 12 s, sys: 626 ms, total: 12.6 s
Wall time: 12.6 s


zones[pd.isna(zones.ZONE_resol)]


zones.ZONE_resol = zones.ZONE_resol.fillna("Special")


zones.GROUPZONE_.value_counts()

General                 52461
Coastal                 42200
Residential             22398
Public Open Space        6667
Business                 3237
Rural                    2750
Special purpose zone      299
New growth                288
Name: GROUPZONE_, dtype: int64


zones.ZONE_resol.value_counts()

Road                                                         47012
Coastal - General Coastal Marine Zone                        26326
Coastal - Coastal Transition Zone                            15703
Residential - Mixed Housing Suburban Zone                     9775
Residential - Mixed Housing Urban Zone                        5864
Strategic Transport Corridor Zone                             4215
Residential - Single House Zone                               3766
Open Space - Conservation Zone                                3063
Open Space - Informal Recreation Zone                         2926
Residential - Terrace Housing and Apartment Building Zone     2134
Rural - Rural Production Zone                                 1025
Water                                                         1013
Business - Mixed Use Zone                                      782
Rural - Rural Coastal Zone                                     768
Business - Light Industry Zone                                 593
Business - Neighbourhood Centre Zone                           564
Residential - Rural and Coastal Settlement Zone                480
Open Space - Sport and Active Recreation Zone                  477
Business - Town Centre Zone                                    432
Residential - Large Lot Zone                                   379
Rural - Mixed Rural Zone                                       344
Rural - Countryside Living Zone                                326
Future Urban Zone                                              282
Business - Local Centre Zone                                   266
Hauraki Gulf Islands                                           221
Business - City Centre Zone                                    191
Open Space - Community Zone                                    183
Rural - Waitakere Ranges Zone                                  172
Business - Metropolitan Centre Zone                            160
Business - Heavy Industry Zone                                 130
Special Purpose - School Zone                                  126
Coastal - Mooring Zone                                         124
Business - General Business Zone                               103
Rural - Rural Conservation Zone                                 71
Rural - Waitakere Foothills Zone                                44
Special Purpose - Cemetery Zone                                 41
Special Purpose - Quarry Zone                                   34
Special Purpose - Maori Purpose Zone                            32
Special Purpose - Major Recreation Facility Zone                28
Special Purpose - Healthcare Facility and Hospital Zone         23
Coastal - Marina Zone                                           20
Coastal - Ferry Terminal Zone                                   20
Open Space - Civic Spaces Zone                                  18
Business - Business Park Zone                                   16
Special Purpose - Airports and Airfields Zone                    9
Green Infrastructure Corridor                                    6
Special Purpose - Tertiary Education Zone                        4
Coastal - Minor Port Zone                                        4
Coastal - Defence Zone                                           3
Special                                                          2
Name: ZONE_resol, dtype: int64


# Create a dictionary mapping the first n character of the zone description to the desired variable name
zones_of_interest = {
    "Residential": "Residential_area",
    "Residential - Single House Zone": "SH_area",
    "Residential - Mixed Housing Suburban Zone": "MHS_area",
    "Residential - Mixed Housing Urban Zone": "MHU_area",
    "Residential - Terrace Housing and Apartment Building Zone": "THA_area",
    "Residential - Large Lot Zone": "LL_area",
    "Future Urban Zone": "FU_area",
    "Hauraki Gulf Islands":  "HGI_area",
    "Business": "Business_area",
    "Rural": "Rural_area",
    "Open Space": "Open_area"
}


%%time
for k,v in tqdm(zones_of_interest.items()):
    subset = zones[zones.ZONE_resol.str.startswith(k)] # Just the matched zones (residential, business, rural etc)
    print(k, len(subset))
    clipped = gpd.clip(df, subset) # Clip the local areas to the matched zones
    df[v] = clipped.area # Store the resulting area. Unit is m²

Residential 22398
Residential - Single House Zone 3766
Residential - Mixed Housing Suburban Zone 9775
Residential - Mixed Housing Urban Zone 5864
Residential - Terrace Housing and Apartment Building Zone 2134
Residential - Large Lot Zone 379
Future Urban Zone 282
Hauraki Gulf Islands 221
Business 3237
Rural 2750
Open Space 6667
CPU times: user 5min 57s, sys: 247 ms, total: 5min 58s
Wall time: 5min 57s


df = df.fillna(0)


skytower = Point(1757109.809, 5920500.841) # in NZGD2000 projection


df["Hdist_skytower"] = df.centroid.distance(skytower) # Unit is meters


df.plot(column="Hdist_skytower", legend=True)

<AxesSubplot:>


coastline = df.dissolve().boundary.iloc[0]


df["Coast_indicator"] = df.intersects(coastline)
df[["Name", "Coast_indicator"]]


households = pd.read_csv("input/2013-mb-dataset-Total-New-Zealand-Household.csv", encoding='unicode_escape')
households

/home/nyou045/git/nodebook/nodebook/nodebookcore.py:283: DtypeWarning: Columns (1,2) have mixed types.Specify dtype option on import or set low_memory=False.
  exec(compile(block, '<string>', mode='exec'), env)


key = "2013_Census_total_household_income_(grouped)(2)(3)(4)_for_households_in_occupied_private_dwellings_Median_household_income_($)(18)(23)"
households_with_income = households[households.Area_Code_and_Description.str.startswith("MB") & (households[key] != "..C") & (households[key] != "*")].copy()
print(len(households_with_income))
households_with_income[key] = households_with_income[key].astype(int)
households_with_income[key].plot(kind="hist", bins=20, figsize=(10,10))

40300

<AxesSubplot:ylabel='Frequency'>


meshblocks = gpd.read_file("input/statsnzpopulation-by-meshblock-2013-census-FGDB.zip!population-by-meshblock-2013-census.gdb")
meshblocks


meshblocks.geometry = meshblocks.representative_point()


meshblocks = meshblocks.merge(households, left_on="Meshblock", right_on="Area_Code_and_Description")


meshblocks = gpd.sjoin(df, meshblocks, op="intersects")
meshblocks


group = meshblocks.groupby("OBJECTID")


df["Census2013_population"] = group["Population_Count_Usual_Resident_2013"].sum()


df["Census2013_dwellings"] = group["2013_Census_total_households_in_occupied_private_dwellings"].sum()


meshblocks_with_income = meshblocks[(meshblocks[key] != "..C") & (meshblocks[key] != "*")].copy()
meshblocks_with_income[key] = meshblocks_with_income[key].astype(int)
meshblocks_with_income


meshblocks_with_income["income*dwellings"] = meshblocks_with_income[key] * meshblocks_with_income["2013_Census_total_households_in_occupied_private_dwellings"]
meshblocks_with_income["income*dwellings"]

OBJECTID
1     1240800.0
1     1350000.0
1     4717500.0
1     4440000.0
1     2550000.0
        ...    
33    2565000.0
33    3622500.0
33    3900000.0
33    1836000.0
33     930600.0
Name: income*dwellings, Length: 10830, dtype: float64


group = meshblocks_with_income.groupby("OBJECTID")
df["Census2013_avg_HH_income"] = group["income*dwellings"].sum() / group["2013_Census_total_households_in_occupied_private_dwellings"].sum()


df[["Name", "Census2013_population", "Census2013_dwellings", "Census2013_avg_HH_income"]].sort_values(by="Census2013_population", ascending=False)

df


df.drop(columns="geometry").to_csv("output/Local_Area.csv")


mb = gpd.read_file("input/statsnzmeshblock-higher-geographies-2018-generalised-FGDB.zip!meshblock-higher-geographies-2018-generalised.gdb")


mb = mb[(mb.TA2018_V1_00_NAME == "Auckland") & mb.LANDWATER_NAME.isin(["Mainland", "Island"])]


mb = mb.rename(columns={"MB2018_V1_00": "Code"})
mb["Centroid_lon"] = mb.centroid.to_crs(epsg=4326).x
mb["Centroid_lat"] = mb.centroid.to_crs(epsg=4326).y
mb


mb = mb.set_index("Code")


skytower = Point(1757109.809, 5920500.841)
mb["Hdist_skytower"] = mb.centroid.distance(skytower) # meters
mb


mb[mb.Hdist_skytower < 5000].plot(column="Hdist_skytower", legend=True)
plt.title("Meshblocks <5KM from the Skytower")

Text(0.5, 1.0, 'Meshblocks <5KM from the Skytower')


coastline = gpd.read_file("input/lds-nz-coastlines-and-islands-polygons-topo-150k-FGDB.zip!nz-coastlines-and-islands-polygons-topo-150k.gdb")


%%time
coastline_shape = coastline.dissolve().boundary.iloc[0]

CPU times: user 34.9 s, sys: 18.9 ms, total: 34.9 s
Wall time: 34.9 s


%%time
mb["Hdist_coast"] = mb.centroid.distance(coastline_shape)

CPU times: user 3min 5s, sys: 17.1 ms, total: 3min 5s
Wall time: 3min 5s


mb["Hdist_coast"].describe() # meters

count    13441.000000
mean      1596.010824
std       1792.477098
min          0.488582
25%        444.163564
50%       1076.033671
75%       2116.959738
max      16027.664069
Name: Hdist_coast, dtype: float64


ax = mb.plot(column="Hdist_coast", legend=True)
gpd.clip(coastline, mb.dissolve().envelope).boundary.plot(ax=ax)

<AxesSubplot:>


len(mb)

13441


BASE_URL = "http://osrm.auckland-cer.cloud.edu.au"
def drive(points, to):
    points = [f"{point.x},{point.y}" for point in points]
    points = ";".join(points)
    result = requests.get(f"{BASE_URL}/table/v1/driving/{to};{points}?destinations=0&annotations=duration,distance")
    return result.json()


skytower = "174.76218883819053,-36.848429166610735"


%%time
result = drive(points=mb.centroid.to_crs(epsg=4326), to=skytower)

CPU times: user 1.29 s, sys: 251 µs, total: 1.29 s
Wall time: 1min 13s


result.keys()

dict_keys(['code', 'sources', 'destinations', 'durations', 'distances'])


mb["Mdist_skytower"] = [r[0] for r in result["distances"][1:]]
mb["Mdist_skytower"].describe() # Units are meters

count     13441.000000
mean      21083.293989
std       15682.240482
min          59.500000
25%       11506.700000
50%       17201.300000
75%       24218.100000
max      136633.200000
Name: Mdist_skytower, dtype: float64


mb.plot(column="Mdist_skytower", legend=True)

<AxesSubplot:>


mb[mb.Mdist_skytower < 5000].plot(column="Mdist_skytower", legend=True)
plt.title("Meshblocks <5KM driving from the Skytower")

Text(0.5, 1.0, 'Meshblocks <5KM driving from the Skytower')


# Some strange gaps there - let's investigate
points = mb[(mb.Hdist_skytower < 1000) & (mb.Mdist_skytower > 5000)]
points = [f"{point.x},{point.y}" for point in points.centroid.to_crs(epsg=4326)]
points

['174.75624867471396,-36.85383185528419',
 '174.75345762242358,-36.850503483712096',
 '174.75551843174853,-36.8526249602678',
 '174.75734772557055,-36.855642008978414']


routes = []
for point in points:
    route_result = requests.get(f"{BASE_URL}/route/v1/driving/{skytower};{point}?geometries=geojson")
    routes.extend(route_result.json()["routes"])
routes

[{'geometry': {'coordinates': [[174.762021, -36.848507],
    [174.762081, -36.848111],
    [174.761663, -36.847994],
    [174.760279, -36.851092],
    [174.756401, -36.856918],
    [174.756298, -36.858244],
    [174.757545, -36.859416],
    [174.761092, -36.859842],
    [174.763064, -36.860822],
    [174.764566, -36.862759],
    [174.766927, -36.864565],
    [174.768415, -36.867436],
    [174.774277, -36.87211],
    [174.774115, -36.872578],
    [174.773385, -36.872143],
    [174.771509, -36.870028],
    [174.768209, -36.867454],
    [174.766746, -36.864588],
    [174.764693, -36.863068],
    [174.76298, -36.861326],
    [174.761243, -36.860494],
    [174.758161, -36.860144],
    [174.756868, -36.859449],
    [174.755729, -36.85724],
    [174.756435, -36.854967],
    [174.756232, -36.853838]],
   'type': 'LineString'},
  'legs': [{'steps': [],
    'distance': 6623.4,
    'duration': 471.2,
    'summary': '',
    'weight': 482.5}],
  'distance': 6623.4,
  'duration': 471.2,
  'weight_name': 'routability',
  'weight': 482.5},
 {'geometry': {'coordinates': [[174.762021, -36.848507],
    [174.761973, -36.848406],
    [174.762081, -36.848111],
    [174.761663, -36.847994],
    [174.760403, -36.850878],
    [174.75828, -36.854101],
    [174.758023, -36.853973],
    [174.756934, -36.853677],
    [174.756692, -36.853485],
    [174.756478, -36.853123],
    [174.756232, -36.85299],
    [174.755149, -36.853046],
    [174.75503, -36.853003],
    [174.754968, -36.852487],
    [174.754131, -36.851451],
    [174.753566, -36.85047]],
   'type': 'LineString'},
  'legs': [{'steps': [],
    'distance': 1487.9,
    'duration': 201.1,
    'summary': '',
    'weight': 212.4}],
  'distance': 1487.9,
  'duration': 201.1,
  'weight_name': 'routability',
  'weight': 212.4},
 {'geometry': {'coordinates': [[174.762021, -36.848507],
    [174.762081, -36.848111],
    [174.760362, -36.847639],
    [174.756384, -36.84814],
    [174.75646, -36.846119],
    [174.751793, -36.844936],
    [174.749101, -36.843285],
    [174.746338, -36.842436],
    [174.744751, -36.840654],
    [174.742077, -36.838893],
    [174.741435, -36.837888],
    [174.74165, -36.836424],
    [174.749967, -36.823771],
    [174.750195, -36.822768],
    [174.749701, -36.81919],
    [174.751533, -36.813923],
    [174.750898, -36.812294],
    [174.753046, -36.811822],
    [174.753968, -36.812071],
    [174.754297, -36.812877],
    [174.753791, -36.813549],
    [174.75206, -36.814361],
    [174.751215, -36.815353],
    [174.750131, -36.819403],
    [174.750332, -36.822928],
    [174.750102, -36.823818],
    [174.741981, -36.836162],
    [174.74164, -36.837484],
    [174.742147, -36.838632],
    [174.744931, -36.840513],
    [174.746304, -36.842152],
    [174.749683, -36.843296],
    [174.752518, -36.845063],
    [174.752984, -36.845821],
    [174.753572, -36.850065],
    [174.755481, -36.852645]],
   'type': 'LineString'},
  'legs': [{'steps': [],
    'distance': 11247.2,
    'duration': 783.1,
    'summary': '',
    'weight': 794.4}],
  'distance': 11247.2,
  'duration': 783.1,
  'weight_name': 'routability',
  'weight': 794.4},
 {'geometry': {'coordinates': [[174.762021, -36.848507],
    [174.761973, -36.848406],
    [174.762081, -36.848111],
    [174.761663, -36.847994],
    [174.760403, -36.850878],
    [174.758622, -36.85365],
    [174.757737, -36.854764],
    [174.757261, -36.855607]],
   'type': 'LineString'},
  'legs': [{'steps': [],
    'distance': 1022.2,
    'duration': 135,
    'summary': '',
    'weight': 146.3}],
  'distance': 1022.2,
  'duration': 135,
  'weight_name': 'routability',
  'weight': 146.3}]


route_df = pd.read_json(json.dumps(routes))
route_df["geometry"] = route_df['geometry'].apply(shape)
route_df = gpd.GeoDataFrame(route_df)
route_df


ax = mb[mb.Mdist_skytower < 5000].to_crs(epsg=4326).plot(column="Mdist_skytower", legend=True)
route_df[route_df["distance"]>10000].plot(column="distance", legend=True, ax=ax, linewidth=3, cmap="plasma")

<AxesSubplot:>


points = mb[(mb.Hdist_skytower < 1000) & (mb.Mdist_skytower > 5000)]
points = [f"{point.y},{point.x}" for point in points.centroid.to_crs(epsg=4326)]
for point in points:
    print(f"https://map.project-osrm.org/?loc={point}&loc=-36.848429166610735,174.76218883819053&srv=0")

https://map.project-osrm.org/?loc=-36.85383185528419,174.75624867471396&loc=-36.848429166610735,174.76218883819053&srv=0
https://map.project-osrm.org/?loc=-36.850503483712096,174.75345762242358&loc=-36.848429166610735,174.76218883819053&srv=0
https://map.project-osrm.org/?loc=-36.8526249602678,174.75551843174853&loc=-36.848429166610735,174.76218883819053&srv=0
https://map.project-osrm.org/?loc=-36.855642008978414,174.75734772557055&loc=-36.848429166610735,174.76218883819053&srv=0


%%time
north = drive(points=mb.centroid.translate(yoff=-100).to_crs(epsg=4326), to=skytower)
west = drive(points=mb.centroid.translate(xoff=-100).to_crs(epsg=4326), to=skytower)
east = drive(points=mb.centroid.translate(xoff=100).to_crs(epsg=4326), to=skytower)
south = drive(points=mb.centroid.translate(yoff=100).to_crs(epsg=4326), to=skytower)

CPU times: user 6.89 s, sys: 41.7 ms, total: 6.93 s
Wall time: 4min 52s


mb["Mdist_skytower_center"] = [r[0] for r in result["distances"][1:]]
mb["Mdist_skytower_north"] = [r[0] for r in north["distances"][1:]]
mb["Mdist_skytower_west"] = [r[0] for r in west["distances"][1:]]
mb["Mdist_skytower_east"] = [r[0] for r in east["distances"][1:]]
mb["Mdist_skytower_south"] = [r[0] for r in south["distances"][1:]]
mb[["Mdist_skytower_center",
    "Mdist_skytower_north",
    "Mdist_skytower_west",
    "Mdist_skytower_east",
    "Mdist_skytower_south"]].describe()


mb["Mdist_skytower"] = mb[[
    "Mdist_skytower_center",
    "Mdist_skytower_north",
    "Mdist_skytower_west",
    "Mdist_skytower_east",
    "Mdist_skytower_south"
]].min(axis=1)
(mb["Mdist_skytower_center"] - mb["Mdist_skytower"]).describe()

count    13441.000000
mean       270.629648
std        506.630148
min          0.000000
25%         81.500000
50%        131.900000
75%        261.300000
max      12976.900000
dtype: float64


mb[mb.Mdist_skytower < 5000].plot(column="Mdist_skytower", legend=True)
plt.title("Meshblocks <5KM driving from the Skytower")

Text(0.5, 1.0, 'Meshblocks <5KM driving from the Skytower')


mb["Mtime_skytower_center"] = [r[0] for r in result["durations"][1:]]
mb["Mtime_skytower_north"] = [r[0] for r in north["durations"][1:]]
mb["Mtime_skytower_west"] = [r[0] for r in west["durations"][1:]]
mb["Mtime_skytower_east"] = [r[0] for r in east["durations"][1:]]
mb["Mtime_skytower_south"] = [r[0] for r in south["durations"][1:]]
display(mb[[
    "Mtime_skytower_center",
    "Mtime_skytower_north",
    "Mtime_skytower_west",
    "Mtime_skytower_east",
    "Mtime_skytower_south"
]].describe())
mb["Mtime_skytower"] = mb[[
    "Mtime_skytower_center",
    "Mtime_skytower_north",
    "Mtime_skytower_west",
    "Mtime_skytower_east",
    "Mtime_skytower_south"
]].min(axis=1)
mb["Mtime_skytower"].describe() # Units are seconds

count    13441.000000
mean      1568.247095
std       3357.297496
min          7.200000
25%        841.300000
50%       1138.400000
75%       1485.000000
max      70521.800000
Name: Mtime_skytower, dtype: float64


mb[mb.Mtime_skytower < 60*20].plot(column="Mtime_skytower", legend=True)
plt.title("Meshblocks < 20 minutes drive from the Skytower")

Text(0.5, 1.0, 'Meshblocks < 20 minutes drive from the Skytower')


transit = pd.read_excel("input/Geocoordinates_Direct_Transit_stops_AKL.xlsx")
transit = gpd.GeoDataFrame(transit, geometry=gpd.points_from_xy(transit.Longitude, transit.Latitude), crs="EPSG:4326")
transit


len(mb)

13441


%%time
def drive(from_points, to_points):
    # Drive distance and time these meshblock centroids to all 58 rapid transit stops
    from_points_s = ";".join([f"{point.x},{point.y}" for point in from_points])
    from_indices = ";".join([str(i) for i in range(len(from_points))])
    to_points_s = ";".join([f"{point.x},{point.y}" for point in to_points])
    to_indices = ";".join([str(i + len(from_points)) for i in range(len(to_points))])
    result = requests.get(f"{BASE_URL}/table/v1/driving/{from_points_s};{to_points_s}?sources={from_indices}&destinations={to_indices}&annotations=duration,distance")
    return result.json()
result = drive(from_points=mb.centroid.to_crs(epsg=4326), to_points=transit.geometry.to_crs(epsg=4326))

CPU times: user 1.77 s, sys: 39.2 ms, total: 1.81 s
Wall time: 30.7 s


min_indices = np.argmin(result["distances"], axis=1)
mb["Mdist_rapid_transit"] = [result["distances"][i][min_indices[i]] for i in range(len(min_indices))]
mb["Mtime_rapid_transit"] = [result["durations"][i][min_indices[i]] for i in range(len(min_indices))]
mb["Mdist_rapid_transit_name"] = list(transit.Location[min_indices])


mb.Mdist_rapid_transit_name.value_counts()

Constellation Bus Station     1006
Hibiscus Coast Bus Station     862
Half Moon Bay                  718
Pukekohe                       515
Manukau                        503
Papakura                       499
Glen Innes                     424
Onehunga                       420
Manurewa                       378
Kingsland                      371
Mt. Albert                     346
West Harbour                   332
Glen Eden                      326
Middlemore                     314
Avondale                       304
Henderson                      288
Papatoetoe                     286
Fruitvale Road                 248
Britomart                      246
Otahuhu                        246
Morningside                    227
Beach Haven                    223
Akoranga Bus Station           223
Sunnyvale                      219
Sylvia Park                    218
Mt Eden                        216
Smales Farm Bus Station        214
Ranui                          208
New Lynn                       202
Gulf Harbour                   193
Newmarket                      185
Orakei                         182
Sturges Road                   177
Homai                          154
Birkenhead                     147
Matiatia                       144
Takanini                       128
Ellerslie                      125
Panmure                        122
Swanson                        121
Meadowbank                     118
Devonport                      115
Grafton                        101
Te Papapa                       96
Bayswater                       95
Remuera                         91
Pine Harbour                    90
Parnell                         90
Puhinui                         83
Penrose                         81
Baldwin Avenue                  68
Northcote                       68
Hobsonville                     56
Stanley Bay                     15
Te Mahia                        11
Sunnynook Bus Station            2
Downtown                         1
Name: Mdist_rapid_transit_name, dtype: int64


ax = mb.to_crs(transit.crs).plot(column="Mdist_rapid_transit", legend=True)
transit.plot(ax=ax, color="red")

<AxesSubplot:>


ax = mb[mb.Mdist_rapid_transit < 5000].to_crs(transit.crs).plot(column="Mdist_rapid_transit", legend=True)
transit.plot(ax=ax, color="red")

<AxesSubplot:>


onramps = gpd.read_file("input/kx-nz-state-highway-on-ramps-off-ramps-SHP.zip")
onramps = gpd.clip(onramps.to_crs(mb.crs), mb)
onramps


# Filter out offramps
onramps = onramps[~onramps.DESCRIPTIO.str.contains("OFF")]
onramps


onramps["has_on"] = onramps.DESCRIPTIO.str.contains("ON").astype(str)
onramps.plot(column="has_on", legend=True)

<AxesSubplot:>


%%time
result = drive(from_points=mb.centroid.to_crs(epsg=4326), to_points=onramps.centroid.to_crs(epsg=4326))

CPU times: user 2.34 s, sys: 82.1 ms, total: 2.42 s
Wall time: 1min


len(result["distances"]), len(result["distances"][0])

(13441, 173)


min_indices = np.argmin(result["distances"], axis=1)
mb["Mdist_onramp"] = [result["distances"][i][min_indices[i]] for i in range(len(min_indices))]
mb["Mtime_onramp"] = [result["durations"][i][min_indices[i]] for i in range(len(min_indices))]
mb["Mdist_onramp_name"] = list(onramps.DESCRIPTIO.iloc[min_indices])


mb.Mdist_onramp_name.value_counts()

01N-2431/07.16-X438-R4-ON    719
016-0007/04.39-I-ON          663
01N-0434-R6                  575
01N-2431/13.34-X444-R2-ON    560
016-0007/06.46-I-ON1         487
                            ... 
016-0019-R2                    1
01N-2398/15.45-X414-R4-ON      1
018-0008-R1                    1
01N-0389-R4                    1
016-0000/08.53-X8-R4-ON        1
Name: Mdist_onramp_name, Length: 115, dtype: int64


ax = mb.plot(column="Mdist_onramp", legend=True)
onramps.plot(ax=ax, color="red")

<AxesSubplot:>


ax = mb[mb.Mdist_onramp < 5000].plot(column="Mdist_onramp", legend=True)
onramps.plot(ax=ax, color="red")

<AxesSubplot:>


coastline = gpd.read_file("input/lds-nz-coastlines-and-islands-polygons-topo-150k-FGDB.zip!nz-coastlines-and-islands-polygons-topo-150k.gdb")


%%time
coastline = gpd.clip(coastline, mb.dissolve().envelope)

CPU times: user 17.3 s, sys: 9.96 ms, total: 17.3 s
Wall time: 17.3 s


%%time
coastline = coastline.dissolve().boundary.buffer(100)
coastline.plot()

CPU times: user 1min 18s, sys: 140 ms, total: 1min 18s
Wall time: 1min 18s

<AxesSubplot:>


coastline = coastline.iloc[0]


%%time
mb["Coast_indicator"] = mb.geometry.progress_apply(lambda poly: coastline.intersects(poly))

CPU times: user 16min 54s, sys: 1.29 s, total: 16min 55s
Wall time: 16min 52s


mb.plot(column="Coast_indicator", legend=True)

<AxesSubplot:>


# Note that this is meshblock 2020, which is not ideal, but there doesn't seem to be any changes in the Auckland region between 2018 and 2020, so it's probably fine
pop = pd.read_csv("input/2018-census-electoral-population-meshblock-2020-data.csv", index_col=0)
pop


pop.General_Electoral_Population = pop.General_Electoral_Population.replace(-999, np.nan)


mb["Census2018_population"] = list(pop.General_Electoral_Population[mb.index.astype(int)])
display(mb["Census2018_population"].describe())
mb.plot(column="Census2018_population", legend=True)

count    12887.000000
mean       113.744859
std         63.156071
min          6.000000
25%         72.000000
50%        108.000000
75%        147.000000
max       1029.000000
Name: Census2018_population, dtype: float64

<AxesSubplot:>


# Can't do this one due to missing data

mb


mb.drop(columns="geometry").to_csv("output/2018_Meshblocks.csv")


sa2 = gpd.read_file("input/statsnzstatistical-area-2-higher-geographies-2018-clipped-generalis-FGDB.zip!statistical-area-2-higher-geographies-2018-clipped-generalis.gdb")
sa2 = sa2[sa2.TA2018_V1_00_NAME == "Auckland"]
sa2


sa2 = sa2.rename(columns={"SA22018_V1_00_NAME": "Name", "SA22018_V1_00": "Code"})
sa2["Centroid_lon"] = sa2.centroid.to_crs(epsg=4326).x
sa2["Centroid_lat"] = sa2.centroid.to_crs(epsg=4326).y
sa2


sa2["Centroid_local_area"] = sa2.representative_point().apply(lambda point: df.Name[df.contains(point)].iloc[0] if any(df.contains(point)) else np.nan)
sa2["Centroid_local_area"].value_counts()

Henderson-Massey                    37
Auckland Central                    32
Beach Haven-Birkenhead-Northcote    28
Manurewa                            28
Orakei                              27
Mangere-Otahuhu                     26
Whau                                26
Botany                              23
Upper Harbour Local Board Area      22
Papakura                            22
Hibiscus Coast                      22
Puketapapa                          20
Devonport-Takapuna                  19
Franklin-Pukekohe                   17
Mt Albert                           16
Tamaki                              16
Papatoetoe                          16
Mt Eden                             15
East Coast Bays                     15
Pakuranga                           14
Rodney-Helensville                  13
Titirangi                           12
Howick                              12
Maungakiekie                        11
Franklin-Beachlands-Hunua           11
Otara                               10
Rodney-Warkworth                     9
Waitakere                            9
Franklin-Waiuku                      7
Waiheke                              7
Rodney-Kumeu-Riverhead               5
Rodney-Dairy Flat                    4
Rodney-Wellsford                     4
Name: Centroid_local_area, dtype: int64


sa2[pd.isna(sa2.Centroid_local_area)]


skytower = Point(1757109.809, 5920500.841) # in NZGD2000 projection


sa2["Hdist_skytower"] = sa2.centroid.distance(skytower)


sa2.plot(column="Hdist_skytower", legend=True)

<AxesSubplot:>


mway = gpd.read_file("input/lds-nz-road-centrelines-topo-150k-FGDB.zip!nz-road-centrelines-topo-150k.gdb")
mway = mway[~pd.isna(mway.hway_num)]
mway


%%time
sa2["Hdist_motorway"] = sa2.centroid.distance(mway.dissolve().geometry[0])
ax = sa2.plot(column="Hdist_motorway", legend=True)
gpd.clip(mway, sa2.envelope).plot(ax=ax, color="red")

CPU times: user 2.3 s, sys: 130 ms, total: 2.43 s
Wall time: 2.28 s

<AxesSubplot:>


rail = gpd.read_file("input/lds-nz-railway-centrelines-topo-150k-SHP.zip")
rail


%%time
sa2["Hdist_rail"] = sa2.centroid.distance(rail.dissolve().geometry[0])
ax = sa2.plot(column="Hdist_rail", legend=True)
gpd.clip(rail, sa2.envelope).plot(ax=ax, color="red")

CPU times: user 3.18 s, sys: 170 ms, total: 3.35 s
Wall time: 3.21 s

<AxesSubplot:>


skytower = gpd.GeoSeries(skytower, crs="EPSG:2193")
skytower

0    POINT (1757109.809 5920500.841)
dtype: geometry


%%time
result = drive(sa2.centroid.to_crs(epsg=4326), skytower.to_crs(epsg=4326))

CPU times: user 314 ms, sys: 28 µs, total: 314 ms
Wall time: 2 s


sa2["Mdist_skytower"] = [r[0] for r in result["distances"]]
sa2["Mtime_skytower"] = [r[0] for r in result["durations"]]


sa2.plot(column="Mdist_skytower", legend=True)

<AxesSubplot:>


sa2[sa2.Mdist_skytower < 5000].plot(column="Mdist_skytower", legend=True)

<AxesSubplot:>


%%time
north = drive(sa2.centroid.translate(yoff=-100).to_crs(epsg=4326), skytower.to_crs(epsg=4326))
west = drive(sa2.centroid.translate(xoff=-100).to_crs(epsg=4326), skytower.to_crs(epsg=4326))
east = drive(sa2.centroid.translate(xoff=100).to_crs(epsg=4326), skytower.to_crs(epsg=4326))
south = drive(sa2.centroid.translate(yoff=100).to_crs(epsg=4326), skytower.to_crs(epsg=4326))

CPU times: user 1.07 s, sys: 30 ms, total: 1.1 s
Wall time: 7.23 s


sa2["Mdist_skytower_center"] = [r[0] for r in result["distances"]]
sa2["Mdist_skytower_north"] = [r[0] for r in north["distances"]]
sa2["Mdist_skytower_west"] = [r[0] for r in west["distances"]]
sa2["Mdist_skytower_east"] = [r[0] for r in east["distances"]]
sa2["Mdist_skytower_south"] = [r[0] for r in south["distances"]]
sa2[["Mdist_skytower_center",
    "Mdist_skytower_north",
    "Mdist_skytower_west",
    "Mdist_skytower_east",
    "Mdist_skytower_south"]].describe()


sa2["Mdist_skytower"] = sa2[[
    "Mdist_skytower_center",
    "Mdist_skytower_north",
    "Mdist_skytower_west",
    "Mdist_skytower_east",
    "Mdist_skytower_south"
]].min(axis=1)
(sa2["Mdist_skytower_center"] - sa2["Mdist_skytower"]).describe()

count     556.000000
mean      240.382914
std       433.754953
min         0.000000
25%        85.350000
50%       118.050000
75%       229.500000
max      5626.500000
dtype: float64


sa2[sa2.Mdist_skytower < 5000].plot(column="Mdist_skytower", legend=True)
plt.title("SA2 <5KM driving from the Skytower")

Text(0.5, 1.0, 'SA2 <5KM driving from the Skytower')


sa2["Mtime_skytower_center"] = [r[0] for r in result["durations"]]
sa2["Mtime_skytower_north"] = [r[0] for r in north["durations"]]
sa2["Mtime_skytower_west"] = [r[0] for r in west["durations"]]
sa2["Mtime_skytower_east"] = [r[0] for r in east["durations"]]
sa2["Mtime_skytower_south"] = [r[0] for r in south["durations"]]
display(sa2[[
    "Mtime_skytower_center",
    "Mtime_skytower_north",
    "Mtime_skytower_west",
    "Mtime_skytower_east",
    "Mtime_skytower_south"
]].describe())
sa2["Mtime_skytower"] = sa2[[
    "Mtime_skytower_center",
    "Mtime_skytower_north",
    "Mtime_skytower_west",
    "Mtime_skytower_east",
    "Mtime_skytower_south"
]].min(axis=1)
sa2["Mtime_skytower"].describe() # Units are seconds

count      556.000000
mean      1584.266547
std       3312.415212
min         46.600000
25%        862.250000
50%       1173.800000
75%       1543.900000
max      70470.600000
Name: Mtime_skytower, dtype: float64


sa2[sa2.Mtime_skytower < 60*20].plot(column="Mtime_skytower", legend=True)
plt.title("SA2s < 20 minutes drive from the Skytower")

Text(0.5, 1.0, 'SA2s < 20 minutes drive from the Skytower')


%%time
result = drive(from_points=sa2.centroid.to_crs(epsg=4326), to_points=transit.geometry.to_crs(epsg=4326))

CPU times: user 273 ms, sys: 22 µs, total: 273 ms
Wall time: 1.6 s


min_indices = np.argmin(result["distances"], axis=1)
sa2["Mdist_rapid_transit"] = [result["distances"][i][min_indices[i]] for i in range(len(min_indices))]
sa2["Mtime_rapid_transit"] = [result["durations"][i][min_indices[i]] for i in range(len(min_indices))]
sa2["Mdist_rapid_transit_name"] = list(transit.Location[min_indices])


sa2.Mdist_rapid_transit_name.value_counts()

Constellation Bus Station     41
Hibiscus Coast Bus Station    36
Half Moon Bay                 30
Manukau                       27
Papakura                      23
Pukekohe                      23
West Harbour                  18
Onehunga                      17
Britomart                     15
Middlemore                    15
Glen Innes                    14
Manurewa                      14
Mt. Albert                    13
Avondale                      12
Kingsland                     12
Otahuhu                       12
Sunnyvale                     11
Papatoetoe                    11
Henderson                     11
Homai                         10
Sylvia Park                   10
Smales Farm Bus Station        9
Fruitvale Road                 9
New Lynn                       9
Morningside                    9
Glen Eden                      9
Ranui                          9
Swanson                        8
Gulf Harbour                   8
Sturges Road                   8
Beach Haven                    7
Mt Eden                        7
Akoranga Bus Station           7
Takanini                       7
Grafton                        6
Newmarket                      6
Ellerslie                      6
Matiatia                       6
Birkenhead                     5
Orakei                         5
Meadowbank                     4
Panmure                        4
Puhinui                        4
Pine Harbour                   4
Bayswater                      4
Parnell                        3
Remuera                        3
Northcote                      3
Devonport                      3
Baldwin Avenue                 2
Hobsonville                    2
Te Papapa                      2
Stanley Bay                    1
Penrose                        1
Te Mahia                       1
Name: Mdist_rapid_transit_name, dtype: int64


ax = sa2.to_crs(transit.crs).plot(column="Mdist_rapid_transit", legend=True)
transit.plot(ax=ax, color="red")

<AxesSubplot:>


ax = sa2[sa2.Mdist_rapid_transit < 5000].to_crs(transit.crs).plot(column="Mdist_rapid_transit", legend=True)
transit.plot(ax=ax, color="red")

<AxesSubplot:>


%%time
result = drive(from_points=sa2.centroid.to_crs(epsg=4326), to_points=onramps.centroid.to_crs(epsg=4326))

CPU times: user 369 ms, sys: 197 µs, total: 369 ms
Wall time: 2.59 s


min_indices = np.argmin(result["distances"], axis=1)
sa2["Mdist_onramp"] = [result["distances"][i][min_indices[i]] for i in range(len(min_indices))]
sa2["Mtime_onramp"] = [result["durations"][i][min_indices[i]] for i in range(len(min_indices))]
sa2["Mdist_onramp_name"] = list(onramps.DESCRIPTIO.iloc[min_indices])


ax = sa2.plot(column="Mdist_onramp", legend=True)
onramps.plot(ax=ax, color="red")

<AxesSubplot:>


ax = sa2[sa2.Mdist_onramp < 5000].plot(column="Mdist_onramp", legend=True)
onramps.plot(ax=ax, color="red")

<AxesSubplot:>


sa2 = sa2.rename(columns={"LAND_AREA_SQ_KM": "Area"})


%%time
for k,v in tqdm(zones_of_interest.items()):
    subset = zones[zones.ZONE_resol.str.startswith(k)] # Just the matched zones (residential, business, rural etc)
    print(k, len(subset))
    clipped = gpd.clip(sa2, subset) # Clip the local areas to the matched zones
    sa2[v] = clipped.area # Store the resulting area. Unit is m²

Residential 22398
Residential - Single House Zone 3766
Residential - Mixed Housing Suburban Zone 9775
Residential - Mixed Housing Urban Zone 5864
Residential - Terrace Housing and Apartment Building Zone 2134
Residential - Large Lot Zone 379
Future Urban Zone 282
Hauraki Gulf Islands 221
Business 3237
Rural 2750
Open Space 6667
CPU times: user 10min 40s, sys: 159 ms, total: 10min 40s
Wall time: 10min 39s


cols = list(zones_of_interest.values())
sa2[cols] = sa2[cols].fillna(0)


%%time
sa2["Coast_indicator"] = sa2.geometry.progress_apply(lambda poly: coastline.intersects(poly))

CPU times: user 48.9 s, sys: 49.8 ms, total: 49 s
Wall time: 48.9 s


sa2.plot(column="Coast_indicator", legend=True, edgecolor="red")

<AxesSubplot:>


pop = pd.read_csv("input/Individual_part1_totalNZ-wide_format_updated_16-7-20.csv")


pop = pop.set_index("Area_code")
pop


sa2["Census2018_population"] = pop.Census_2018_usually_resident_population_count[sa2.Code].tolist()
sa2.plot(column="Census2018_population", legend=True)

<AxesSubplot:>


sa2[["Name", "Census2018_population"]].sort_values("Census2018_population", ascending=False).head(20)


sa2.Census2018_population.plot(kind="hist", bins=100)

<AxesSubplot:ylabel='Frequency'>


dwellings = pd.read_excel("input/2018_census_dwellings_by_SA2.xlsx", sheet_name="Table 1", skiprows=11)
dwellings = dwellings[~pd.isna(dwellings.Total)]
dwellings.index = dwellings["Unnamed: 0"].str.split(" ").str[0]
dwellings = dwellings.replace("..C", np.nan)
dwellings


sa2["Census2018_dwellings"] = dwellings.Total[sa2.Code].tolist()
sa2["Census2018_avg_HH_income"] = dwellings["Mean Household \nIncome ($)"][sa2.Code].tolist()
sa2["Census2018_med_HH_income"] = dwellings["Median Household\n Income ($)"][sa2.Code].tolist()


sa2.plot(column="Census2018_dwellings", legend=True)
sa2.plot(column="Census2018_avg_HH_income", legend=True)
sa2.plot(column="Census2018_med_HH_income", legend=True)

<AxesSubplot:>


sa2.Census2018_med_HH_income.plot(kind="hist", bins=100)

<AxesSubplot:ylabel='Frequency'>

sa2


sa2.drop(columns="geometry").to_csv("output/2018_Statistical_Area_2.csv")


au = gpd.read_file("input/statsnzarea-unit-2013-FGDB.zip!area-unit-2013.gdb")


au = au.rename(columns={"AU2013_V1_00_NAME": "Name", "AU2013_V1_00": "Code"})
au["Centroid_lon"] = au.centroid.to_crs(epsg=4326).x
au["Centroid_lat"] = au.centroid.to_crs(epsg=4326).y


au["Centroid_local_area"] = au.representative_point().apply(lambda point: df.Name[df.contains(point)].iloc[0] if any(df.contains(point)) else np.nan)
problems = au.Code.isin(["520602", "521135", "506634"])
au.at[problems, "Centroid_local_area"] = au[problems].geometry.apply(lambda poly: df.Name[df.intersects(poly)].iloc[0])
au["Centroid_local_area"].value_counts()

Henderson-Massey                    29
Manurewa                            21
Orakei                              20
Auckland Central                    20
Mangere-Otahuhu                     19
Beach Haven-Birkenhead-Northcote    19
Hibiscus Coast                      17
Whau                                16
Rodney-Helensville                  15
Upper Harbour Local Board Area      15
Papakura                            14
Devonport-Takapuna                  13
Mt Eden                             13
Botany                              13
Franklin-Pukekohe                   12
Titirangi                           12
Rodney-Warkworth                    12
Papatoetoe                          11
Pakuranga                           11
Puketapapa                          11
Tamaki                              11
Mt Albert                           11
East Coast Bays                     11
Franklin-Beachlands-Hunua           10
Howick                              10
Maungakiekie                         9
Otara                                9
Waitakere                            8
Franklin-Waiuku                      5
Rodney-Kumeu-Riverhead               4
Waiheke                              3
Rodney-Wellsford                     3
Rodney-Dairy Flat                    2
Name: Centroid_local_area, dtype: int64


print(f'{sum(~pd.isna(au["Centroid_local_area"]))} matches, {sum(pd.isna(au["Centroid_local_area"]))} unmatched')

409 matches, 1595 unmatched


au = au[~pd.isna(au.Centroid_local_area)]
ax = au.plot(column="Centroid_local_area", cmap="Spectral", legend=True)


au["Hdist_skytower"] = au.distance(skytower.iloc[0])
au.plot(column="Hdist_skytower", legend=True)

<AxesSubplot:>


%%time
au["Hdist_motorway"] = au.centroid.distance(mway.dissolve().geometry[0])
ax = au.plot(column="Hdist_motorway", legend=True)
gpd.clip(mway, au.envelope).plot(ax=ax, color="red")

CPU times: user 1.84 s, sys: 281 ms, total: 2.13 s
Wall time: 1.98 s

<AxesSubplot:>


%%time
au["Hdist_rail"] = au.centroid.distance(rail.dissolve().geometry[0])
ax = au.plot(column="Hdist_rail", legend=True)
gpd.clip(rail, au.envelope).plot(ax=ax, color="red")

CPU times: user 2.92 s, sys: 150 ms, total: 3.07 s
Wall time: 2.92 s

<AxesSubplot:>


%%time
result = drive(au.centroid.to_crs(epsg=4326), skytower.to_crs(epsg=4326))
north = drive(au.centroid.translate(yoff=-100).to_crs(epsg=4326), skytower.to_crs(epsg=4326))
west = drive(au.centroid.translate(xoff=-100).to_crs(epsg=4326), skytower.to_crs(epsg=4326))
east = drive(au.centroid.translate(xoff=100).to_crs(epsg=4326), skytower.to_crs(epsg=4326))
south = drive(au.centroid.translate(yoff=100).to_crs(epsg=4326), skytower.to_crs(epsg=4326))

CPU times: user 1.16 s, sys: 9.82 ms, total: 1.17 s
Wall time: 5.85 s


au["Mdist_skytower_center"] = [r[0] for r in result["distances"]]
au["Mdist_skytower_north"] = [r[0] for r in north["distances"]]
au["Mdist_skytower_west"] = [r[0] for r in west["distances"]]
au["Mdist_skytower_east"] = [r[0] for r in east["distances"]]
au["Mdist_skytower_south"] = [r[0] for r in south["distances"]]
display(au[[
    "Mdist_skytower_center",
    "Mdist_skytower_north",
    "Mdist_skytower_west",
    "Mdist_skytower_east",
    "Mdist_skytower_south"
]].describe())
au["Mdist_skytower"] = au[[
    "Mdist_skytower_center",
    "Mdist_skytower_north",
    "Mdist_skytower_west",
    "Mdist_skytower_east",
    "Mdist_skytower_south"
]].min(axis=1)
display(au["Mdist_skytower"].describe())

au["Mtime_skytower_center"] = [r[0] for r in result["durations"]]
au["Mtime_skytower_north"] = [r[0] for r in north["durations"]]
au["Mtime_skytower_west"] = [r[0] for r in west["durations"]]
au["Mtime_skytower_east"] = [r[0] for r in east["durations"]]
au["Mtime_skytower_south"] = [r[0] for r in south["durations"]]
display(au[[
    "Mtime_skytower_center",
    "Mtime_skytower_north",
    "Mtime_skytower_west",
    "Mtime_skytower_east",
    "Mtime_skytower_south"
]].describe())
au["Mtime_skytower"] = au[[
    "Mtime_skytower_center",
    "Mtime_skytower_north",
    "Mtime_skytower_west",
    "Mtime_skytower_east",
    "Mtime_skytower_south"
]].min(axis=1)
display(au["Mtime_skytower"].describe())

count      409.000000
mean     22531.981907
std      16385.776202
min        446.000000
25%      12227.900000
50%      18255.700000
75%      27187.300000
max      87888.800000
Name: Mdist_skytower, dtype: float64

count      409.000000
mean      1459.823227
std       1263.493685
min         73.100000
25%        883.600000
50%       1203.900000
75%       1588.300000
max      14753.900000
Name: Mtime_skytower, dtype: float64


au.plot(column="Mdist_skytower", legend=True)

<AxesSubplot:>


au[au.Mdist_skytower < 10000].plot(column="Mdist_skytower", legend=True)

<AxesSubplot:>


%%time
result = drive(from_points=au.centroid.to_crs(epsg=4326), to_points=transit.geometry.to_crs(epsg=4326))

CPU times: user 212 ms, sys: 39.6 ms, total: 252 ms
Wall time: 1.24 s


min_indices = np.argmin(result["distances"], axis=1)
au["Mdist_rapid_transit"] = [result["distances"][i][min_indices[i]] for i in range(len(min_indices))]
au["Mtime_rapid_transit"] = [result["durations"][i][min_indices[i]] for i in range(len(min_indices))]
au["Mdist_rapid_transit_name"] = list(transit.Location[min_indices])


au.Mdist_rapid_transit_name.value_counts()

Hibiscus Coast Bus Station    36
Constellation Bus Station     25
Half Moon Bay                 24
Papakura                      21
West Harbour                  18
Pukekohe                      15
Manukau                       13
Papatoetoe                    12
Otahuhu                       12
Kingsland                     11
Manurewa                      11
Ranui                         10
Onehunga                      10
Glen Innes                    10
Sunnyvale                     10
Mt. Albert                    10
Henderson                      9
Middlemore                     9
Glen Eden                      9
Swanson                        8
Avondale                       7
Morningside                    7
Sylvia Park                    6
Homai                          6
New Lynn                       6
Beach Haven                    6
Fruitvale Road                 6
Grafton                        6
Britomart                      5
Smales Farm Bus Station        5
Akoranga Bus Station           5
Orakei                         5
Newmarket                      4
Gulf Harbour                   4
Birkenhead                     4
Ellerslie                      4
Mt Eden                        4
Panmure                        3
Te Papapa                      3
Remuera                        3
Bayswater                      3
Sturges Road                   3
Takanini                       3
Parnell                        2
Matiatia                       2
Devonport                      2
Northcote                      2
Puhinui                        2
Pine Harbour                   2
Baldwin Avenue                 2
Stanley Bay                    1
Penrose                        1
Meadowbank                     1
Te Mahia                       1
Name: Mdist_rapid_transit_name, dtype: int64


ax = au.to_crs(transit.crs).plot(column="Mdist_rapid_transit", legend=True)
transit.plot(ax=ax, color="red")

<AxesSubplot:>


ax = au[au.Mdist_rapid_transit < 10000].to_crs(transit.crs).plot(column="Mdist_rapid_transit", legend=True)
transit.plot(ax=ax, color="red")

<AxesSubplot:>


%%time
result = drive(from_points=au.centroid.to_crs(epsg=4326), to_points=onramps.centroid.to_crs(epsg=4326))

CPU times: user 313 ms, sys: 29.6 ms, total: 343 ms
Wall time: 1.82 s


min_indices = np.argmin(result["distances"], axis=1)
au["Mdist_onramp"] = [result["distances"][i][min_indices[i]] for i in range(len(min_indices))]
au["Mtime_onramp"] = [result["durations"][i][min_indices[i]] for i in range(len(min_indices))]
au["Mdist_onramp_name"] = list(onramps.DESCRIPTIO.iloc[min_indices])


ax = au.plot(column="Mdist_onramp", legend=True)
onramps.plot(ax=ax, color="red")

<AxesSubplot:>


ax = au[au.Mdist_onramp < 5000].plot(column="Mdist_onramp", legend=True)
onramps.plot(ax=ax, color="red")

<AxesSubplot:>


au = au.rename(columns={"LAND_AREA_SQ_KM": "Area"})


%%time
for k,v in tqdm(zones_of_interest.items()):
    subset = zones[zones.ZONE_resol.str.startswith(k)] # Just the matched zones (residential, business, rural etc)
    print(k, len(subset))
    clipped = gpd.clip(au, subset) # Clip the local areas to the matched zones
    au[v] = clipped.area # Store the resulting area. Unit is m²

Residential 22398
Residential - Single House Zone 3766
Residential - Mixed Housing Suburban Zone 9775
Residential - Mixed Housing Urban Zone 5864
Residential - Terrace Housing and Apartment Building Zone 2134
Residential - Large Lot Zone 379
Future Urban Zone 282
Hauraki Gulf Islands 221
Business 3237
Rural 2750
Open Space 6667
CPU times: user 10min 2s, sys: 1.97 s, total: 10min 3s
Wall time: 10min 3s


au = au.fillna(0)


%%time
au["Coast_indicator"] = au.geometry.progress_apply(lambda poly: coastline.intersects(poly))

CPU times: user 30.7 s, sys: 150 ms, total: 30.8 s
Wall time: 30.7 s


au.plot(column="Coast_indicator", legend=True, edgecolor="red")

<AxesSubplot:>


pop = pd.read_csv("input/2013-mb-dataset-Total-New-Zealand-Individual-Part-1.csv", encoding='unicode_escape', low_memory=False)
pop


pop = pop.set_index("Code")


au["Census2013_population"] = pop["2013_Census_census_usually_resident_population_count(1)"][au.Code].tolist()


au.plot(column="Census2013_population", legend=True)

<AxesSubplot:>


households = households.set_index("Code")


au["Census2013_dwellings"] = households["2013_Census_total_households_in_occupied_private_dwellings"][au.Code].tolist()


au.plot(column="Census2013_dwellings", legend=True)

<AxesSubplot:>


# Can't do this one due to missing data

key

'2013_Census_total_household_income_(grouped)(2)(3)(4)_for_households_in_occupied_private_dwellings_Median_household_income_($)(18)(23)'


au["Census2013_med_HH_income"] = households[key][au.Code].replace("..C", np.nan).astype(float).tolist()


au.plot(column="Census2013_med_HH_income", legend=True)

<AxesSubplot:>

au


au.drop(columns="geometry").to_csv("output/2013_Area_Unit.csv")


zones


cols = [
    "Residential - Terrace Housing and Apartment Building Zone",
    "Residential - Mixed Housing Urban Zone",
    "Residential - Mixed Housing Suburban Zone"
]


coastline = gpd.read_file("input/lds-nz-coastlines-and-islands-polygons-topo-150k-FGDB.zip!nz-coastlines-and-islands-polygons-topo-150k.gdb")


cmap = matplotlib.colors.ListedColormap(['red', 'blue', 'green'])
ax = zones[zones.ZONE_resol.isin(cols)].plot(column="ZONE_resol", legend=True, cmap=cmap)
xmin, xmax = ax.get_xlim()
ymin, ymax = ax.get_ylim()
print(xmin, xmax, ymin, ymax)
coastline.boundary.plot(ax=ax, color="gray", linewidth=.5)
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin + 1e4, ymax - 4e4)

ax.add_artist(ScaleBar(1, location="lower left"))

x, y, arrow_length = 0.05, .98, 0.05
ax.annotate('N', xy=(x, y), xytext=(x, y-arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)

ax.set_xticks([])
ax.set_yticks([])

1735024.9733500003 1779731.6056499996 5869626.39383 5980785.90757

[]


ax = zones[zones.ZONE_resol == "Residential - Single House Zone"].plot(column="ZONE_resol", legend=True, cmap=cmap)
xmin, xmax = ax.get_xlim()
ymin, ymax = ax.get_ylim()
print(xmin, xmax, ymin, ymax)
coastline.boundary.plot(ax=ax, color="gray", linewidth=.5)
ax.set_xlim(xmin + 1e4, xmax - 1e4)
ax.set_ylim(ymin + 1e4, ymax - 4e4)

ax.add_artist(ScaleBar(1, location="lower left"))

x, y, arrow_length = 0.05, .98, 0.05
ax.annotate('N', xy=(x, y), xytext=(x, y-arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)
ax.set_xticks([])
ax.set_yticks([])

1724205.46675 1797169.8142499996 5869706.9597 5992439.5543

[]


df.Name[df.Hdist_skytower < 2e4].tolist()

['Auckland Central',
 'Beach Haven-Birkenhead-Northcote',
 'Botany',
 'Devonport-Takapuna',
 'East Coast Bays',
 'Henderson-Massey',
 'Howick',
 'Mangere-Otahuhu',
 'Maungakiekie',
 'Mt Albert',
 'Mt Eden',
 'Orakei',
 'Otara',
 'Pakuranga',
 'Papatoetoe',
 'Puketapapa',
 'Tamaki',
 'Titirangi',
 'Upper Harbour Local Board Area',
 'Whau']


df["Core"] = df.Name.isin(['Auckland Central',
 'Beach Haven-Birkenhead-Northcote',
 'Botany',
 'Devonport-Takapuna',
 'East Coast Bays',
 'Henderson-Massey',
 'Howick',
 'Mangere-Otahuhu',
 'Manurewa',
 'Maungakiekie',
 'Mt Albert',
 'Mt Eden',
 'Orakei',
 'Otara',
 'Pakuranga',
 'Papatoetoe',
 'Papakura',
 'Puketapapa',
 'Tamaki',
 'Titirangi',
 'Upper Harbour Local Board Area',
 'Whau'])
display(df[["Name", "Core"]])


cmap = matplotlib.colors.ListedColormap(['orange', 'yellow'])
ax = df.plot(column = "Core", cmap=cmap, edgecolor="black", linewidth=.5)
xmin, xmax = ax.get_xlim()
ymin, ymax = ax.get_ylim()
print(xmin, xmax, ymin, ymax)
coastline.boundary.plot(ax=ax, color="gray", linewidth=.5)
ax.set_xlim(xmin, xmax)
ax.set_ylim(ymin, ymax)

ax.add_artist(ScaleBar(1, location="lower left"))

x, y, arrow_length = 0.05, .98, 0.05
ax.annotate('N', xy=(x, y), xytext=(x, y-arrow_length),
            arrowprops=dict(facecolor='black', width=5, headwidth=15),
            ha='center', va='center', fontsize=20,
            xycoords=ax.transAxes)
ax.set_xticks([])
ax.set_yticks([])

1699039.1745048524 1809972.3879951476 5864874.988694763 6008322.51060524

[]

	name	filesize (MB)	last modified
0	2013-mb-dataset-Total-New-Zealand-Household.csv	37.12	2014-06-04 10:56:30.000000
1	2013-mb-dataset-Total-New-Zealand-Individual-Part-1.csv	31.66	2014-04-01 10:13:15.197000
2	2018-census-electoral-population-meshblock-2020-data.csv	5.60	2021-08-09 00:39:18.000000
3	2018_census_dwellings_by_SA2.xlsx	0.43	2021-07-12 14:30:28.000000
4	AC_Special_Housing_Area.zip	0.29	2021-09-02 10:36:16.760000
5	AucklandArea.gpkg	0.09	2021-09-02 10:36:17.220000
6	Geocoordinates_Direct_Transit_stops_AKL.xlsx	0.01	2020-10-08 07:52:29.000000
7	Individual_part1_totalNZ-wide_format_updated_16-7-20.csv	36.58	2020-07-14 16:12:24.000000
8	MASTER_UP_BaseZone_SHP.zip	66.92	2021-07-19 02:23:51.137347
9	Modified_Community_Boards_SHP.zip	1.30	2021-07-19 02:16:07.650000
10	area-unit-2013.gdb.zip	14.21	2021-09-02 10:36:16.070000
11	kx-nz-state-highway-on-ramps-off-ramps-SHP.zip	0.14	2021-08-25 09:52:06.341291
12	lds-nz-coastline-mean-high-water-FGDB.zip	4.88	2021-08-31 16:25:16.250000
13	lds-nz-coastlines-and-islands-polygons-topo-150k-FGDB.zip	4.25	2021-08-05 15:57:49.020000
14	lds-nz-primary-parcels-CLIPPED-4326.gpkg	273.35	2021-09-02 10:37:32.410000
15	lds-nz-primary-parcels-FGDB.zip	79.03	2021-08-12 09:13:27.910000
16	lds-nz-railway-centrelines-topo-150k-SHP.zip	0.87	2021-08-31 10:01:46.315326
17	lds-nz-road-centrelines-topo-150k-FGDB.zip	36.06	2021-08-31 09:43:07.931704
18	lds-nz-street-address-GPKG-CLIPPED.gpkg	170.23	2021-09-02 10:37:19.490000
19	lds-nz-street-address-GPKG.zip	200.88	2021-09-02 10:37:26.070000
20	meshblock-2013.gdb.zip	106.57	2021-09-02 10:36:59.860000
21	meshblock-2018-clipped-generalised.gdb.zip	32.83	2021-09-02 10:36:26.010000
22	nz-primary-parcels.gdb.zip	55.00	2021-09-02 10:36:39.020000
23	statsnzarea-unit-2013-FGDB.zip	13.79	2021-08-31 16:56:17.150000
24	statsnzmeshblock-higher-geographies-2018-generalised-FGDB.zip	34.54	2021-08-05 14:53:54.350000
25	statsnzpopulation-by-meshblock-2013-census-FGDB.zip	82.11	2021-07-19 13:53:55.150631
26	statsnzstatistical-area-2-higher-geographies-2018-clipped-generalis-FGDB.zip	10.72	2021-08-30 17:12:55.591895

	OBJECTID	CONTOUR	created_da	DocumentUR	GlobalID	GROUPZONE	GROUPZONE_	last_edite	NAME	PARCEL_BAS	...	TYPE	TYPE_resol	VERSIONSTA	VERSIONS_1	ZONE	ZONE_resol	ZONEHEIGHT	SHAPE_Leng	SHAPE_Area	geometry
0	1.0	None	20160718211	None	{4C8F9436-7EA6-417E-B64F-15FCD44459F6}	2	Residential	20161111010	None	None	...	None	None	4	Operative	60	Residential - Mixed Housing Urban Zone	NaN	285.664016	2.050275e+03	POLYGON ((1768030.306 5901206.846, 1768033.070...
1	2.0	None	20160718211	None	{604AAD87-8ED4-4111-8276-47CEE7E81F92}	1	Public Open Space	20161111010	None	None	...	None	None	4	Operative	33	Open Space - Sport and Active Recreation Zone	NaN	1246.837757	1.684599e+04	POLYGON ((1764267.286 5919989.370, 1764218.153...
2	3.0	None	20160718211	None	{8D827DA8-BC5B-437A-B17A-532354F7D037}	4	Rural	20161111010	None	None	...	None	None	4	Operative	11	Rural - Mixed Rural Zone	NaN	3582.113246	6.841744e+05	POLYGON ((1740091.195 5928308.839, 1740089.844...
3	4.0	None	20160718211	None	{96C9E266-3341-4C71-94F1-325F2EE45732}	2	Residential	20161111010	None	None	...	None	None	4	Operative	23	Residential - Large Lot Zone	NaN	317.098469	6.024226e+03	POLYGON ((1750502.125 5928677.635, 1750456.131...
4	5.0	None	20160718211	None	{90B50FEE-45A3-4E88-819A-370751ACDE3D}	1	Public Open Space	20161111010	None	None	...	None	None	4	Operative	31	Open Space - Conservation Zone	NaN	230.836636	5.639794e+02	POLYGON ((1741460.217 5918191.600, 1741476.745...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
130295	130296.0	None	20161115151	None	{B2F0FB45-80F6-41ED-AA57-A914B195B31E}	1	Public Open Space	20161115151	None	None	...	None	None	4	Operative	31	Open Space - Conservation Zone	NaN	179382.307787	9.040193e+07	POLYGON ((1738072.631 5902593.421, 1738095.386...
130296	130297.0	None	20161115151	None	{A3F4EF61-9162-43C3-90BD-DC84D93C6A64}	2	Residential	20161115151	None	None	...	None	None	4	Operative	18	Residential - Mixed Housing Suburban Zone	NaN	2769.292040	2.920602e+05	POLYGON ((1770189.642 5905789.775, 1770199.407...
130297	130298.0	None	20161115151	None	{A5FC7EB5-76E5-414C-96DD-715C671764B4}	4	Rural	20161115151	Kaipara South Head and Harbour coastal area	None	...	None	None	4	Operative	46	Rural - Rural Coastal Zone	NaN	27168.762393	6.344938e+06	POLYGON ((1730253.401 5955767.484, 1730254.399...
130298	130299.0	None	20161115151	None	{C371B83C-35CE-46A1-B94F-F1E592F271F7}	2	Residential	20161115151	None	None	...	None	None	4	Operative	8	Residential - Terrace Housing and Apartment Bu...	NaN	1812.109533	1.536829e+05	POLYGON ((1743032.759 5924130.564, 1742996.886...
130299	130300.0	None	20161115151	None	{31281924-C74D-4038-8260-FE6D886F4C94}	2	Residential	20161115151	None	None	...	None	None	4	Operative	18	Residential - Mixed Housing Suburban Zone	NaN	2511.267945	3.539606e+05	POLYGON ((1748979.767 5925771.895, 1748986.615...

	OBJECTID	CONTOUR	created_da	DocumentUR	GlobalID	GROUPZONE	GROUPZONE_	last_edite	NAME	PARCEL_BAS	...	TYPE	TYPE_resol	VERSIONSTA	VERSIONS_1	ZONE	ZONE_resol	ZONEHEIGHT	SHAPE_Leng	SHAPE_Area	geometry
20853	20854.0	None	20160718211	None	{2D83F680-9587-4D57-AF0B-CB7CA27C3D2C}	6	Special purpose zone	20161111011	None	None	...	None	None	4	Operative	58	None	NaN	72.629617	22.286945	POLYGON ((1767684.860 5903714.023, 1767691.955...
121765	121766.0	None	20160718211	None	{FD3E8BB4-1979-42B5-9A77-5D04AE5190AF}	6	Special purpose zone	20161111010	None	None	...	None	None	4	Operative	58	None	NaN	86.135341	4.114962	POLYGON ((1767684.083 5903713.690, 1767666.954...

	Area_Code_and_Description	Code	Description	2001_Census_total_households_in_occupied_private_dwellings	2006_Census_total_households_in_occupied_private_dwellings	2013_Census_total_households_in_occupied_private_dwellings	2001_Census_household_composition_for_households_in_occupied_private_dwellings_One-family_household_(with_or_without_other_people)	2001_Census_household_composition_for_households_in_occupied_private_dwellings_Two-family_household_(with_or_without_other_people)	2001_Census_household_composition_for_households_in_occupied_private_dwellings_Three_or_more_family_household_(with_or_without_other_people)	2001_Census_household_composition_for_households_in_occupied_private_dwellings_Other_multi-person_household	...	2006_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Not_Elsewhere_Included(22)	2006_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Total_households_in_occupied_private_dwellings	2013_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_No_Access_to_Telecommunication_Systems	2013_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Access_to_a_Cellphone/Mobile_Phone	2013_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Access_to_a_Telephone	2013_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Access_to_a_Fax_Machine	2013_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Access_to_the_Internet	2013_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Total_households_stated	2013_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Not_Elsewhere_Included(22)	2013_Census_access_to_telecommunications(20)(21)_for_households_in_occupied_private_dwellings_Total_households_in_occupied_private_dwellings
0	MB 0000100	100	NaN	3.0	3.0	3.0	..C	..C	..C	..C	...	..C	3.0	..C	..C	..C	..C	..C	..C	..C	3.0
1	MB 0000200	200	NaN	27.0	27.0	30.0	15	3	0	0	...	3	24.0	3	9	24	6	15	30	3	30.0
2	MB 0000300	300	NaN	21.0	24.0	21.0	18	3	0	0	...	0	24.0	0	6	12	3	9	18	3	18.0
3	MB 0000400	400	NaN	9.0	9.0	9.0	6	..C	..C	..C	...	..C	12.0	..C	..C	9	..C	6	9	..C	9.0
4	MB 0000501	501	NaN	0.0	0.0	0.0	..C	..C	..C	..C	...	..C	0.0	..C	..C	..C	..C	..C	..C	..C	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
49038	Footnotes for specific variables or categories	23	Median total household income is rounded to th...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
49039	Symbols	..C	Confidential	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
49040	Symbols	..	not available	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
49041	Symbols	*	not able to be calculated	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
49042	Source	Statistics New Zealand	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	Meshblock	MeshblockNumber	Population_Count_Usual_Resident_2013	Population_Count_Census_Night_2013	geometry
0	MB 0352700	0352700	0	0	MULTIPOLYGON (((1753237.550 5923918.395, 17532...
1	MB 0728500	0728500	9	6	MULTIPOLYGON (((1761011.438 5905840.848, 17610...
2	MB 0829300	0829300	93	99	MULTIPOLYGON (((1739739.656 5899165.556, 17397...
3	MB 1280801	1280801	0	0	MULTIPOLYGON (((1869852.595 5695096.558, 18714...
4	MB 2360001	2360001	0	0	MULTIPOLYGON (((1623601.773 5423210.098, 16235...
...	...	...	...	...	...
46616	MB 0074002	0074002	0	0	MULTIPOLYGON (((1741178.600 6046572.236, 17414...
46617	MB 3208002	3208002	78	81	MULTIPOLYGON (((1770892.430 5911519.906, 17708...
46618	MB 3208003	3208003	36	36	MULTIPOLYGON (((1771025.156 5911674.629, 17709...
46619	MB 3209001	3209001	84	87	MULTIPOLYGON (((1771731.425 5912665.799, 17717...
46620	MB 3209002	3209002	75	81	MULTIPOLYGON (((1771758.103 5912422.760, 17717...

Phase 2: Geographic Datasets¶

Outputs¶

Local area dataset¶

Meshblock 2018 dataset:¶

Statistical Area 2 2018 dataset¶

Area Unit 2013 dataset¶

Maps¶

	Local_Area	geometry
OBJECTID
1	Auckland Central	POLYGON ((1755802.315 5921956.091, 1755861.443...
2	Beach Haven-Birkenhead-Northcote	POLYGON ((1757287.966 5925962.738, 1757211.333...
3	Botany	POLYGON ((1770748.846 5912611.168, 1770889.893...
4	Devonport-Takapuna	POLYGON ((1755276.581 5932026.336, 1755278.305...
5	East Coast Bays	POLYGON ((1756125.006 5940268.048, 1756139.852...
6	Franklin-Beachlands-Hunua	MULTIPOLYGON (((1804302.354 5890738.079, 17905...
7	Franklin-Pukekohe	POLYGON ((1765085.620 5897344.807, 1765096.229...
8	Franklin-Waiuku	POLYGON ((1744829.308 5899882.633, 1744835.760...
9	Henderson-Massey	POLYGON ((1745963.138 5923457.510, 1745945.884...
10	Hibiscus Coast	MULTIPOLYGON (((1752023.352 5954803.281, 17520...
11	Howick	POLYGON ((1771447.460 5916900.636, 1771474.703...
12	Mangere-Otahuhu	MULTIPOLYGON (((1766142.746 5910838.845, 17661...
13	Manurewa	POLYGON ((1769432.488 5904664.673, 1769471.312...
14	Maungakiekie	POLYGON ((1759418.674 5915517.660, 1759596.351...
15	Mt Albert	POLYGON ((1756291.061 5918265.104, 1756266.530...
16	Mt Eden	POLYGON ((1751910.995 5920299.659, 1751917.632...
17	Orakei	POLYGON ((1759833.395 5920429.502, 1759837.143...
18	Otara	MULTIPOLYGON (((1765765.640 5909501.655, 17657...
19	Pakuranga	POLYGON ((1769523.742 5920466.582, 1769527.337...
20	Papakura	POLYGON ((1772188.699 5902202.011, 1772192.566...
21	Papatoetoe	POLYGON ((1765105.421 5908611.893, 1765113.661...
22	Puketapapa	POLYGON ((1753132.892 5915040.177, 1753141.101...
23	Rodney-Dairy Flat	POLYGON ((1746055.938 5948651.765, 1746067.112...
24	Rodney-Helensville	MULTIPOLYGON (((1710567.626 5967865.416, 17106...
25	Rodney-Kumeu-Riverhead	POLYGON ((1742532.808 5931237.574, 1742490.377...
26	Rodney-Warkworth	MULTIPOLYGON (((1761692.564 5984690.018, 17617...
27	Rodney-Wellsford	MULTIPOLYGON (((1746318.204 6000215.757, 17464...
28	Tamaki	POLYGON ((1765594.677 5917986.938, 1765609.808...
29	Titirangi	POLYGON ((1749038.038 5910572.842, 1749034.230...
30	Upper Harbour Local Board Area	MULTIPOLYGON (((1743880.554 5928979.708, 17438...
31	Waiheke	MULTIPOLYGON (((1793981.864 5931917.843, 17940...
32	Waitakere	MULTIPOLYGON (((1743890.310 5905057.188, 17438...
33	Whau	MULTIPOLYGON (((1748168.244 5916597.058, 17481...

	Name	Coast_indicator
OBJECTID
1	Auckland Central	True
2	Beach Haven-Birkenhead-Northcote	True
3	Botany	True
4	Devonport-Takapuna	True
5	East Coast Bays	True
6	Franklin-Beachlands-Hunua	True
7	Franklin-Pukekohe	True
8	Franklin-Waiuku	True
9	Henderson-Massey	True
10	Hibiscus Coast	True
11	Howick	True
12	Mangere-Otahuhu	True
13	Manurewa	True
14	Maungakiekie	True
15	Mt Albert	False
16	Mt Eden	True
17	Orakei	True
18	Otara	True
19	Pakuranga	True
20	Papakura	True
21	Papatoetoe	True
22	Puketapapa	True
23	Rodney-Dairy Flat	True
24	Rodney-Helensville	True
25	Rodney-Kumeu-Riverhead	True
26	Rodney-Warkworth	True
27	Rodney-Wellsford	True
28	Tamaki	True
29	Titirangi	True
30	Upper Harbour Local Board Area	True
31	Waiheke	True
32	Waitakere	True
33	Whau	True

	Name	Census2013_population	Census2013_dwellings	Census2013_avg_HH_income
OBJECTID
9	Henderson-Massey	107658	34461.0	68347.842761
2	Beach Haven-Birkenhead-Northcote	82431	28344.0	80154.552207
13	Manurewa	82230	22584.0	69202.861326
17	Orakei	79581	29001.0	110143.425140
1	Auckland Central	77058	31506.0	86499.398108
33	Whau	72585	23928.0	65948.529781
12	Mangere-Otahuhu	70998	17427.0	61855.553636
4	Devonport-Takapuna	55398	20307.0	89560.517751
30	Upper Harbour Local Board Area	53571	17079.0	92354.882984
22	Puketapapa	52959	16695.0	75460.348858
15	Mt Albert	47913	15630.0	97670.686671
3	Botany	46878	13893.0	93325.464764
16	Mt Eden	46752	16155.0	83313.629602
20	Papakura	45687	14919.0	68043.070707
21	Papatoetoe	45633	13272.0	65055.631090
10	Hibiscus Coast	45141	17397.0	70786.765722
5	East Coast Bays	44634	15354.0	96602.171362
28	Tamaki	42189	13551.0	64390.277469
11	Howick	40299	13836.0	85281.267636
19	Pakuranga	39924	13206.0	82096.046353
29	Titirangi	35958	12192.0	80713.729357
7	Franklin-Pukekohe	31176	10863.0	78094.119279
18	Otara	30033	6693.0	58229.733394
14	Maungakiekie	27780	10368.0	76915.212333
6	Franklin-Beachlands-Hunua	21111	7332.0	97416.701031
24	Rodney-Helensville	17832	6381.0	78202.750119
26	Rodney-Warkworth	17637	6912.0	62686.367596
8	Franklin-Waiuku	13581	5046.0	68150.358852
32	Waitakere	12477	4449.0	84773.121192
31	Waiheke	8310	3597.0	55066.946309
25	Rodney-Kumeu-Riverhead	7257	2472.0	86943.048780
23	Rodney-Dairy Flat	6564	2130.0	106361.971831
27	Rodney-Wellsford	5595	2115.0	51240.028490

	Code	SA12018_V1_00	SA22018_V1_00	SA22018_V1_00_NAME	UR2018_V1_00	UR2018_V1_00_NAME	IUR2018_V1_00	IUR2018_V1_00_NAME	CB2018_V1_00	CB2018_V1_00_NAME	...	WARD2018_V1_00	WARD2018_V1_00_NAME	LANDWATER	LANDWATER_NAME	LAND_AREA_SQ_KM	AREA_SQ_KM	Shape_Length	geometry	Centroid_lon	Centroid_lat
543	0137200	7001157	110400	Cape Rodney	1098	Other rural Auckland	22	Rural other	07601	Rodney Local Board Area	...	07601	Rodney Ward	11	Island	0.097567	0.097567	1356.404986	MULTIPOLYGON (((1761489.925 5985285.678, 17614...	174.797456	-36.265557
544	0170700	7001317	114300	Gulf Islands	1098	Other rural Auckland	22	Rural other	07602	Hibiscus and Bays Local Board Area	...	07602	Albany Ward	11	Island	0.005545	0.005545	321.185380	MULTIPOLYGON (((1753753.763 5954256.206, 17537...	174.718386	-36.545155
545	0438700	7001130	111800	Barrier Islands	1098	Other rural Auckland	22	Rural other	07608	Great Barrier Local Board Area	...	07605	Waitemata and Gulf Ward	11	Island	70.725594	70.725594	100201.649851	MULTIPOLYGON (((1815954.125 6007877.906, 18159...	175.382085	-36.108549
546	0439306	7001135	111800	Barrier Islands	1109	Tryphena	21	Rural settlement	07608	Great Barrier Local Board Area	...	07605	Waitemata and Gulf Ward	11	Island	0.078233	0.078233	1494.370224	MULTIPOLYGON (((1823569.424 5979782.392, 18235...	175.489541	-36.302663
547	0439307	7001135	111800	Barrier Islands	1109	Tryphena	21	Rural settlement	07608	Great Barrier Local Board Area	...	07605	Waitemata and Gulf Ward	11	Island	0.475337	0.475337	3408.621475	MULTIPOLYGON (((1824128.365 5979223.530, 18242...	175.494343	-36.309033
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
53299	4011884	7001155	110400	Cape Rodney	1073	Whangateau	21	Rural settlement	07601	Rodney Local Board Area	...	07601	Rodney Ward	12	Mainland	1.161280	1.161280	7266.530123	MULTIPOLYGON (((1759217.632 5980700.819, 17596...	174.769327	-36.310203
53301	4011883	7001154	110400	Cape Rodney	1098	Other rural Auckland	22	Rural other	07601	Rodney Local Board Area	...	07601	Rodney Ward	12	Mainland	7.680889	7.680889	17076.458542	MULTIPOLYGON (((1756196.931 5982761.262, 17562...	174.752390	-36.303185
53333	4011925	7009199	156900	Baverstock	1108	Auckland	11	Major urban area	07616	Howick Local Board Area	...	07610	Howick Ward	12	Mainland	0.242189	0.242189	2352.598074	MULTIPOLYGON (((1770681.901 5909197.803, 17711...	174.920613	-36.949951
53359	4011971	7009313	158600	Ormiston East	1108	Auckland	11	Major urban area	07616	Howick Local Board Area	...	07610	Howick Ward	12	Mainland	0.487587	0.487587	3692.342983	MULTIPOLYGON (((1771223.196 5906518.069, 17715...	174.930444	-36.975090
53360	4011972	7009335	158900	Tuscany Heights	1108	Auckland	11	Major urban area	07616	Howick Local Board Area	...	07610	Howick Ward	12	Mainland	0.178460	0.178460	1798.203501	MULTIPOLYGON (((1772642.183 5906246.394, 17726...	174.940243	-36.976625

	geometry	legs	distance	duration	weight_name	weight
0	LINESTRING (174.76202 -36.84851, 174.76208 -36...	[{'steps': [], 'distance': 6623.4, 'duration':...	6623.4	471.2	routability	482.5
1	LINESTRING (174.76202 -36.84851, 174.76197 -36...	[{'steps': [], 'distance': 1487.9, 'duration':...	1487.9	201.1	routability	212.4
2	LINESTRING (174.76202 -36.84851, 174.76208 -36...	[{'steps': [], 'distance': 11247.2, 'duration'...	11247.2	783.1	routability	794.4
3	LINESTRING (174.76202 -36.84851, 174.76197 -36...	[{'steps': [], 'distance': 1022.2, 'duration':...	1022.2	135.0	routability	146.3

	Mdist_skytower_center	Mdist_skytower_north	Mdist_skytower_west	Mdist_skytower_east	Mdist_skytower_south
count	13441.000000	13441.000000	13441.000000	13441.000000	13441.000000
mean	21083.293989	21104.614404	21076.932974	21080.920289	21044.649126
std	15682.240482	15672.937314	15680.363394	15684.086849	15692.827461
min	59.500000	29.900000	110.700000	155.200000	109.600000
25%	11506.700000	11517.900000	11485.800000	11514.800000	11467.600000
50%	17201.300000	17168.500000	17202.300000	17154.200000	17146.600000
75%	24218.100000	24276.600000	24203.500000	24235.700000	24178.300000
max	136633.200000	136633.200000	136633.200000	136633.200000	136633.200000

	Location	Latitude	Longitude	geometry
0	West Harbour	-36.810902	174.645571	POINT (174.64557 -36.81090)
1	Hobsonville	-36.787563	174.672260	POINT (174.67226 -36.78756)
2	Beach Haven	-36.789800	174.678317	POINT (174.67832 -36.78980)
3	Birkenhead	-36.822961	174.733882	POINT (174.73388 -36.82296)
4	Northcote	-36.826891	174.746329	POINT (174.74633 -36.82689)
5	Bayswater	-36.821697	174.766296	POINT (174.76630 -36.82170)
6	Stanley Bay	-36.828009	174.781236	POINT (174.78124 -36.82801)
7	Downtown	-36.842912	174.766930	POINT (174.76693 -36.84291)
8	Devonport	-36.833343	174.795631	POINT (174.79563 -36.83334)
9	Matiatia	-36.780617	174.991460	POINT (174.99146 -36.78062)
10	Pine Harbour	-36.889582	174.989955	POINT (174.98996 -36.88958)
11	Half Moon Bay	-36.879913	174.897656	POINT (174.89766 -36.87991)
12	Gulf Harbour	-36.624279	174.787779	POINT (174.78778 -36.62428)
13	Avondale	-36.897302	174.699092	POINT (174.69909 -36.89730)
14	Baldwin Avenue	-36.877694	174.720462	POINT (174.72046 -36.87769)
15	Britomart	-36.844177	174.767766	POINT (174.76777 -36.84418)
16	Ellerslie	-36.898225	174.808085	POINT (174.80809 -36.89822)
17	Fruitvale Road	-36.910636	174.667006	POINT (174.66701 -36.91064)
18	Glen Eden	-36.910149	174.652854	POINT (174.65285 -36.91015)
19	Glen Innes	-36.878788	174.854118	POINT (174.85412 -36.87879)
20	Grafton	-36.865501	174.770008	POINT (174.77001 -36.86550)
21	Greenlane	-36.889649	174.797444	POINT (174.79744 -36.88965)
22	Henderson	-36.880903	174.630923	POINT (174.63092 -36.88090)
23	Homai	-37.013442	174.874655	POINT (174.87465 -37.01344)
24	Kingsland	-36.872443	174.744520	POINT (174.74452 -36.87244)
25	Manukau	-36.993769	174.877389	POINT (174.87739 -36.99377)
26	Manurewa	-37.023261	174.896139	POINT (174.89614 -37.02326)
27	Meadowbank	-36.866298	174.820777	POINT (174.82078 -36.86630)
28	Middlemore	-36.963044	174.838964	POINT (174.83896 -36.96304)
29	Morningside	-36.874913	174.735210	POINT (174.73521 -36.87491)
30	Mt Eden	-36.867955	174.758970	POINT (174.75897 -36.86796)
31	Mt. Albert	-36.884793	174.714007	POINT (174.71401 -36.88479)
32	New Lynn	-36.909357	174.684020	POINT (174.68402 -36.90936)
33	Newmarket	-36.869622	174.778879	POINT (174.77888 -36.86962)
34	Onehunga	-36.925383	174.786012	POINT (174.78601 -36.92538)
35	Orakei	-36.862352	174.809466	POINT (174.80947 -36.86235)
36	Otahuhu	-36.946888	174.833261	POINT (174.83326 -36.94689)
37	Panmure	-36.898136	174.849269	POINT (174.84927 -36.89814)
38	Papatoetoe	-36.977583	174.849376	POINT (174.84938 -36.97758)
39	Papakura	-37.064926	174.946311	POINT (174.94631 -37.06493)
40	Parnell	-36.854689	174.777457	POINT (174.77746 -36.85469)
41	Penrose	-36.910093	174.815687	POINT (174.81569 -36.91009)
42	Puhinui	-36.989788	174.856028	POINT (174.85603 -36.98979)
43	Pukekohe	-37.203246	174.910155	POINT (174.91016 -37.20325)
44	Ranui	-36.867874	174.603303	POINT (174.60330 -36.86787)
45	Remuera	-36.881313	174.785404	POINT (174.78540 -36.88131)
46	Sturges Road	-36.873433	174.620874	POINT (174.62087 -36.87343)
47	Sunnyvale	-36.896779	174.631982	POINT (174.63198 -36.89678)
48	Swanson	-36.866032	174.576258	POINT (174.57626 -36.86603)
49	Sylvia Park	-36.914619	174.842589	POINT (174.84259 -36.91462)
50	Takanini	-37.041127	174.919446	POINT (174.91945 -37.04113)
51	Te Mahia	-37.031118	174.906152	POINT (174.90615 -37.03112)
52	Te Papapa	-36.920079	174.801432	POINT (174.80143 -36.92008)
53	Hibiscus Coast Bus Station	-36.624647	174.667158	POINT (174.66716 -36.62465)
54	Constellation Bus Station	-36.752014	174.728166	POINT (174.72817 -36.75201)
55	Sunnynook Bus Station	-36.761481	174.737999	POINT (174.73800 -36.76148)
56	Smales Farm Bus Station	-36.784523	174.751077	POINT (174.75108 -36.78452)
57	Akoranga Bus Station	-36.797232	174.760938	POINT (174.76094 -36.79723)

	ID	ROADID	SH	INTERCHANG	DISPLACEME	RAMPNO	LENGTH	DESCRIPTIO	geometry
1	288.0	288	020	0019	0.00	1	37.0	020-0010/03.65-D-ON	LINESTRING (1758742.502 5911581.839, 1758734.3...
2	251.0	251	016	0027	0.00	1	155.0	016-0007/04.01-I-OFF	LINESTRING (1747446.038 5919555.363, 1747389.5...
5	291.0	291	020	0022	0.00	1	203.0	020-0010/02.28-I-ON	LINESTRING (1759420.418 5910233.008, 1759414.2...
34	1765.0	1765	020	0002	0.00	1	414.0	020-0010/04.77-I-OFF	LINESTRING (1757782.109 5912150.461, 1757752.3...
35	2623.0	2623	01N	2036	0.00	1	337.0	01N-2398/12.36-X410-R3-OFF	LINESTRING (1753039.078 5934634.671, 1753012.1...
...	...	...	...	...	...	...	...	...	...
437	6297867.0	3101	018	0005	None	1	313.0	018-0005-R1	LINESTRING (1750624.813 5929454.552, 1750604.6...
438	6297864.0	3106	018	0002	None	4	263.0	018-0002-R4	LINESTRING (1752046.399 5930728.423, 1752081.5...
439	6297838.0	3086	016	0100	None	2	843.0	016-0003-R2	LINESTRING (1756829.783 5919210.950, 1756778.7...
440	6297836.0	3088	016	0002	None	4	437.0	016-0002-R4 TEMP ON	LINESTRING (1757567.004 5919997.142, 1757604.0...
446	6298167.0	3253	016	0019	None	3	68.0	016-0019-R3	LINESTRING (1743974.769 5923934.492, 1743962.0...

	General_Electoral_Population	Maori_Electoral_Population	GED2020_V1_00	GED2020_V1_00_NAME	GED2020_V1_00_NAME_ASCII	MED2020_V1_00	MED2020_V1_00_NAME	MED2020_V1_00_NAME_ASCII	LAND_AREA_SQ_KM	AREA_SQ_KM
MB2020_V2_00
100	-999	9	32	Northland	Northland	5	Te Tai Tokerau	Te Tai Tokerau	157.497825	157.497825
200	9	66	32	Northland	Northland	5	Te Tai Tokerau	Te Tai Tokerau	120.503770	120.503770
300	12	48	32	Northland	Northland	5	Te Tai Tokerau	Te Tai Tokerau	7.481859	7.481859
400	6	9	32	Northland	Northland	5	Te Tai Tokerau	Te Tai Tokerau	83.342952	83.342952
501	-999	-999	32	Northland	Northland	5	Te Tai Tokerau	Te Tai Tokerau	0.000000	63.825713
...	...	...	...	...	...	...	...	...	...	...
4012026	24	-999	60	Wellington Central	Wellington Central	6	Te Tai Tonga	Te Tai Tonga	8.301605	8.301605
4012027	108	6	56	Waikato	Waikato	1	Hauraki-Waikato	Hauraki-Waikato	5.064385	5.064385
4012028	57	-999	51	Taupō	Taupo	1	Hauraki-Waikato	Hauraki-Waikato	2.612804	2.612804
4012029	-999	-999	47	Taieri	Taieri	6	Te Tai Tonga	Te Tai Tonga	0.000000	143.908480
4012030	-999	-999	8	Dunedin	Dunedin	6	Te Tai Tonga	Te Tai Tonga	0.000000	1016.346178

	SA22018_V1_00	SA22018_V1_00_NAME	REGC2018_V1_00	REGC2018_V1_00_NAME	TA2018_V1_00	TA2018_V1_00_NAME	LAND_AREA_SQ_KM	AREA_SQ_KM	Shape_Length	geometry
33	110900	Dome Valley-Matakana	02	Auckland Region	076	Auckland	84.749966	84.749966	70031.926112	MULTIPOLYGON (((1749212.212 5981874.734, 17492...
34	111100	Warkworth West	02	Auckland Region	076	Auckland	11.359752	11.359752	22629.664017	MULTIPOLYGON (((1748589.106 5974509.939, 17485...
35	111200	Puhoi Valley	02	Auckland Region	076	Auckland	236.529965	236.529965	142134.544465	MULTIPOLYGON (((1740248.959 5974374.891, 17402...
36	112600	Waikoukou Valley	02	Auckland Region	076	Auckland	52.854583	52.854583	42565.729322	MULTIPOLYGON (((1735693.112 5942203.564, 17359...
37	112800	Hatfields Beach	02	Auckland Region	076	Auckland	0.923255	0.923255	4314.440531	MULTIPOLYGON (((1750981.811 5952164.661, 17512...
...	...	...	...	...	...	...	...	...	...	...
1060	165600	Cape Hill	02	Auckland Region	076	Auckland	0.717375	0.717375	3569.522355	MULTIPOLYGON (((1770139.188 5882466.221, 17701...
1061	165800	Rooseville Park	02	Auckland Region	076	Auckland	1.345809	1.345809	6010.945714	MULTIPOLYGON (((1770089.518 5882192.248, 17701...
1062	166000	Pukekohe Central	02	Auckland Region	076	Auckland	2.939129	2.939129	9211.796424	MULTIPOLYGON (((1769469.158 5881504.407, 17694...
1063	166300	Bombay Hills	02	Auckland Region	076	Auckland	31.682020	31.682020	37482.345738	MULTIPOLYGON (((1777896.083 5888522.045, 17779...
1064	166400	Ararimu	02	Auckland Region	076	Auckland	91.308241	91.308241	57552.107386	MULTIPOLYGON (((1786544.313 5891035.131, 17865...

	Name	geometry	Centroid_lon	Centroid_lat	Area	Residential_area	SH_area	MHS_area	MHU_area	THA_area	...	FU_area	HGI_area	Business_area	Rural_area	Open_area	Hdist_skytower	Coast_indicator	Census2013_population	Census2013_dwellings	Census2013_avg_HH_income
OBJECTID
1	Auckland Central	POLYGON ((1755802.315 5921956.091, 1755861.443...	174.753763	-36.855194	1.942334e+07	6.114247e+06	3.264382e+06	1.005111e+06	9.761411e+05	8.686144e+05	...	0.000000e+00	0.000000e+00	4.770257e+06	0.000000e+00	2.505081e+06	1067.517983	True	77058	31506.0	86499.398108
2	Beach Haven-Birkenhead-Northcote	POLYGON ((1757287.966 5925962.738, 1757211.333...	174.721897	-36.794764	3.406603e+07	1.989760e+07	5.073543e+06	1.007797e+07	3.677952e+06	9.609651e+05	...	0.000000e+00	0.000000e+00	2.554498e+06	0.000000e+00	6.442079e+06	6948.094501	True	82431	28344.0	80154.552207
3	Botany	POLYGON ((1770748.846 5912611.168, 1770889.893...	174.917904	-36.957505	3.978830e+07	1.437879e+07	4.003298e+05	9.023214e+06	2.665899e+06	1.402369e+06	...	0.000000e+00	0.000000e+00	6.222613e+06	9.837755e+06	4.056828e+06	18420.075506	True	46878	13893.0	93325.464764
4	Devonport-Takapuna	POLYGON ((1755276.581 5932026.336, 1755278.305...	174.769923	-36.789009	2.111121e+07	1.240583e+07	2.595033e+06	6.760630e+06	2.528557e+06	5.216074e+05	...	0.000000e+00	0.000000e+00	1.165067e+06	0.000000e+00	2.289014e+06	6621.239572	True	55398	20307.0	89560.517751
5	East Coast Bays	POLYGON ((1756125.006 5940268.048, 1756139.852...	174.733122	-36.701804	3.038568e+07	1.402263e+07	2.429714e+06	8.490284e+06	9.649423e+05	2.219014e+05	...	0.000000e+00	0.000000e+00	3.126271e+05	7.860819e+06	3.829094e+06	16467.095662	True	44634	15354.0	96602.171362
6	Franklin-Beachlands-Hunua	MULTIPOLYGON (((1804302.354 5890738.079, 17905...	175.101754	-37.043410	7.778987e+08	7.835879e+06	6.069391e+06	3.296486e+04	0.000000e+00	3.034730e+04	...	1.048487e+07	0.000000e+00	3.582480e+06	4.948783e+08	8.000078e+07	37193.822526	True	21111	7332.0	97416.701031
7	Franklin-Pukekohe	POLYGON ((1765085.620 5897344.807, 1765096.229...	174.864884	-37.151426	2.717220e+08	1.622679e+07	6.467033e+06	5.660850e+06	3.079772e+06	3.792095e+05	...	2.256611e+07	0.000000e+00	1.981925e+06	2.111716e+08	4.301604e+06	34851.656145	True	31176	10863.0	78094.119279
8	Franklin-Waiuku	POLYGON ((1744829.308 5899882.633, 1744835.760...	174.666624	-37.164727	2.997611e+08	7.599913e+06	4.391057e+05	2.379868e+06	1.111391e+05	0.000000e+00	...	1.885628e+05	0.000000e+00	5.024976e+06	2.704684e+08	4.015675e+06	36121.662648	True	13581	5046.0	68150.358852
9	Henderson-Massey	POLYGON ((1745963.138 5923457.510, 1745945.884...	174.621864	-36.853375	5.321961e+07	3.041467e+07	5.403257e+06	6.196787e+06	1.438878e+07	4.167398e+06	...	2.356178e+06	0.000000e+00	5.410361e+06	6.572595e+05	5.824062e+06	12525.129414	True	107658	34461.0	68347.842761
10	Hibiscus Coast	MULTIPOLYGON (((1752023.352 5954803.281, 17520...	174.720160	-36.618022	7.969308e+07	2.547122e+07	1.531569e+07	1.601068e+06	1.640568e+06	4.426904e+05	...	3.334191e+06	0.000000e+00	3.122063e+06	2.421639e+07	1.334561e+07	25832.060623	True	45141	17397.0	70786.765722
11	Howick	POLYGON ((1771447.460 5916900.636, 1771474.703...	174.926939	-36.902134	1.461575e+07	1.060461e+07	3.809348e+06	5.160719e+06	1.340626e+06	2.939136e+05	...	0.000000e+00	0.000000e+00	3.478985e+05	5.208255e+04	1.331571e+06	15854.353763	True	40299	13836.0	85281.267636
12	Mangere-Otahuhu	MULTIPOLYGON (((1766142.746 5910838.845, 17661...	174.794437	-36.972479	5.250979e+07	1.304669e+07	1.231326e+06	6.294665e+06	3.943621e+06	1.366092e+06	...	1.146902e+06	0.000000e+00	8.392959e+06	2.711786e+06	7.736789e+06	14070.305771	True	70998	17427.0	61855.553636
13	Manurewa	POLYGON ((1769432.488 5904664.673, 1769471.312...	174.886011	-37.018487	3.711616e+07	1.691256e+07	1.083351e+06	1.234376e+07	2.890852e+06	5.945951e+05	...	1.208450e+06	0.000000e+00	6.011804e+06	3.124826e+05	6.021889e+06	21865.784349	True	82230	22584.0	69202.861326
14	Maungakiekie	POLYGON ((1759418.674 5915517.660, 1759596.351...	174.799976	-36.915243	1.740899e+07	5.002987e+06	3.103315e+05	2.479102e+06	1.117549e+06	1.096004e+06	...	0.000000e+00	0.000000e+00	6.424025e+06	0.000000e+00	2.818621e+06	8151.195589	True	27780	10368.0	76915.212333
15	Mt Albert	POLYGON ((1756291.061 5918265.104, 1756266.530...	174.765299	-36.884922	1.385353e+07	8.308876e+06	3.715019e+06	2.058002e+06	1.566702e+06	9.691525e+05	...	0.000000e+00	0.000000e+00	1.219666e+06	0.000000e+00	9.752424e+05	4067.283622	False	47913	15630.0	97670.686671
16	Mt Eden	POLYGON ((1751910.995 5920299.659, 1751917.632...	174.718360	-36.879882	1.448821e+07	8.270683e+06	1.092566e+06	2.464966e+06	3.332548e+06	1.380604e+06	...	0.000000e+00	0.000000e+00	1.026338e+06	0.000000e+00	1.709990e+06	5244.560531	True	46752	16155.0	83313.629602
17	Orakei	POLYGON ((1759833.395 5920429.502, 1759837.143...	174.830542	-36.870391	3.234712e+07	1.873652e+07	1.811262e+06	1.210010e+07	3.663284e+06	1.161878e+06	...	0.000000e+00	0.000000e+00	1.326911e+06	0.000000e+00	5.326449e+06	6567.770945	True	79581	29001.0	110143.425140
18	Otara	MULTIPOLYGON (((1765765.640 5909501.655, 17657...	174.883562	-36.963798	1.213648e+07	5.101724e+06	1.985154e+04	1.887588e+06	2.723733e+06	4.705511e+05	...	0.000000e+00	0.000000e+00	2.434656e+06	0.000000e+00	2.367666e+06	16766.406523	True	30033	6693.0	58229.733394
19	Pakuranga	POLYGON ((1769523.742 5920466.582, 1769527.337...	174.891901	-36.898057	1.527965e+07	9.096320e+06	3.916487e+04	7.010512e+06	1.552850e+06	4.937933e+05	...	0.000000e+00	0.000000e+00	5.185541e+05	0.000000e+00	2.768248e+06	12812.703793	True	39924	13206.0	82096.046353
20	Papakura	POLYGON ((1772188.699 5902202.011, 1772192.566...	174.940390	-37.059940	4.022528e+07	1.808030e+07	1.423108e+06	1.187048e+07	4.001187e+06	2.503432e+05	...	6.270214e+06	0.000000e+00	4.066388e+06	2.638487e+06	2.646971e+06	28340.533206	True	45687	14919.0	68043.070707
21	Papatoetoe	POLYGON ((1765105.421 5908611.893, 1765113.661...	174.849291	-36.987020	2.497254e+07	9.380759e+06	1.026630e+06	6.202250e+06	1.652101e+06	4.997783e+05	...	6.587091e+05	0.000000e+00	7.079133e+06	0.000000e+00	3.473963e+06	17234.007915	True	45633	13272.0	65055.631090
22	Puketapapa	POLYGON ((1753132.892 5915040.177, 1753141.101...	174.741502	-36.916029	1.871805e+07	1.108573e+07	7.062543e+05	6.066849e+06	2.942080e+06	1.360481e+06	...	0.000000e+00	0.000000e+00	1.061183e+06	0.000000e+00	3.279811e+06	7732.493593	True	52959	16695.0	75460.348858
23	Rodney-Dairy Flat	POLYGON ((1746055.938 5948651.765, 1746067.112...	174.636168	-36.672259	1.132876e+08	2.928810e+06	4.415845e+05	1.082742e+06	7.851764e+05	1.177736e+05	...	2.571329e+07	0.000000e+00	2.402994e+05	7.596025e+07	3.594364e+06	22546.984200	True	6564	2130.0	106361.971831
24	Rodney-Helensville	MULTIPOLYGON (((1710567.626 5967865.416, 17106...	174.425342	-36.646179	7.974650e+08	5.122441e+06	2.915926e+06	4.710662e+05	8.037448e+04	0.000000e+00	...	4.384484e+05	0.000000e+00	5.046133e+05	7.218524e+08	2.634552e+07	37521.293694	True	17832	6381.0	78202.750119
25	Rodney-Kumeu-Riverhead	POLYGON ((1742532.808 5931237.574, 1742490.377...	174.538408	-36.780645	5.536146e+07	4.569329e+06	3.637463e+06	8.916239e+05	4.024164e+04	0.000000e+00	...	8.770729e+06	0.000000e+00	1.003907e+06	3.686394e+07	8.429452e+05	21330.923764	True	7257	2472.0	86943.048780
26	Rodney-Warkworth	MULTIPOLYGON (((1761692.564 5984690.018, 17617...	174.613063	-36.444991	6.684679e+08	1.681370e+07	5.952212e+06	1.799798e+05	6.208502e+04	0.000000e+00	...	1.266006e+07	0.000000e+00	1.633665e+06	5.541282e+08	3.901882e+07	46699.922300	True	17637	6912.0	62686.367596
27	Rodney-Wellsford	MULTIPOLYGON (((1746318.204 6000215.757, 17464...	174.538530	-36.291690	6.404974e+08	1.680348e+06	1.152092e+06	0.000000e+00	0.000000e+00	0.000000e+00	...	1.064854e+06	0.000000e+00	4.148911e+05	5.748374e+08	3.130978e+07	64927.125388	True	5595	2115.0	51240.028490
28	Tamaki	POLYGON ((1765594.677 5917986.938, 1765609.808...	174.848247	-36.901670	1.896657e+07	8.372005e+06	1.763932e+04	3.137190e+06	2.703557e+06	2.513619e+06	...	0.000000e+00	0.000000e+00	5.024667e+06	0.000000e+00	2.233061e+06	9688.801624	True	42189	13551.0	64390.277469
29	Titirangi	POLYGON ((1749038.038 5910572.842, 1749034.230...	174.629790	-36.939520	4.058868e+07	1.476243e+07	1.672204e+06	2.303941e+06	1.039881e+06	9.614689e+05	...	0.000000e+00	0.000000e+00	4.333662e+05	8.622547e+06	1.203022e+07	15542.090769	True	35958	12192.0	80713.729357
30	Upper Harbour Local Board Area	MULTIPOLYGON (((1743880.554 5928979.708, 17438...	174.672015	-36.760234	6.973017e+07	2.098467e+07	4.506156e+06	7.750997e+06	3.291655e+06	3.947975e+05	...	9.573434e+06	0.000000e+00	6.433695e+06	1.217632e+07	8.853710e+06	12662.494326	True	53571	17079.0	92354.882984
31	Waiheke	MULTIPOLYGON (((1793981.864 5931917.843, 17940...	175.055216	-36.799894	1.547768e+08	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	...	0.000000e+00	1.521384e+08	0.000000e+00	0.000000e+00	0.000000e+00	26689.652804	True	8310	3597.0	55066.946309
32	Waitakere	MULTIPOLYGON (((1743890.310 5905057.188, 17438...	174.527874	-36.931281	2.651435e+08	3.686649e+06	4.685951e+05	1.370472e+05	2.243794e+05	0.000000e+00	...	2.606228e+05	0.000000e+00	5.718099e+04	8.375348e+07	1.672914e+08	22820.718458	True	12477	4449.0	84773.121192
33	Whau	MULTIPOLYGON (((1748168.244 5916597.058, 17481...	174.684472	-36.906902	2.682152e+07	1.602986e+07	9.520285e+05	6.562767e+06	6.248986e+06	2.009848e+06	...	0.000000e+00	0.000000e+00	3.309943e+06	0.000000e+00	2.842184e+06	9497.105591	True	72585	23928.0	65948.529781

	Mtime_skytower_center	Mtime_skytower_north	Mtime_skytower_west	Mtime_skytower_east	Mtime_skytower_south
count	13441.000000	13441.000000	13441.000000	13441.000000	13441.000000
mean	1594.367316	1593.642854	1592.224001	1592.324723	1590.448925
std	3358.619612	3358.274390	3358.655563	3357.112345	3358.303358
min	15.100000	7.200000	26.700000	38.900000	37.900000
25%	867.000000	863.700000	865.100000	862.400000	862.400000
50%	1163.400000	1160.600000	1160.800000	1160.200000	1159.400000
75%	1514.500000	1510.900000	1510.600000	1511.700000	1508.300000
max	70521.800000	70521.800000	70521.800000	70521.800000	70521.800000

	t50_fid	name_ascii	macronated	name	hway_num	rna_sufi	lane_count	way_count	status	surface	geometry
46	100120164	OHURA ROAD	N	OHURA ROAD	43	3063799	2	None	None	sealed	MULTILINESTRING ((1726407.913 5648240.420, 172...
56	100047464	STATE HIGHWAY 27	N	STATE HIGHWAY 27	27	3031731	2	None	None	sealed	MULTILINESTRING ((1844110.982 5802925.948, 184...
62	100047472	STATE HIGHWAY 29A	N	STATE HIGHWAY 29A	29A	3025693	2	None	None	sealed	MULTILINESTRING ((1876759.340 5818523.003, 187...
68	100047481	STATE HIGHWAY 2	N	STATE HIGHWAY 2	2	3045160	2	None	None	sealed	MULTILINESTRING ((1875605.931 5823398.039, 187...
77	100089761	WEST COAST ROAD	N	WEST COAST ROAD	73	3072232	2	None	None	sealed	MULTILINESTRING ((1490507.178 5234796.972, 149...
...	...	...	...	...	...	...	...	...	...	...	...
138626	100121061	STATE HIGHWAY 5	N	STATE HIGHWAY 5	5	3068040	2	None	None	sealed	MULTILINESTRING ((1884714.508 5730000.000, 188...
138635	100121070	CHRISTCHURCH SOUTHERN MOTORWAY	N	CHRISTCHURCH SOUTHERN MOTORWAY	76	3061191	4	None	None	sealed	MULTILINESTRING ((1555118.439 5176036.767, 155...
138636	100121071	WESTERN BELFAST BYPASS	N	WESTERN BELFAST BYPASS	1	3079158	4	None	None	sealed	MULTILINESTRING ((1567931.555 5188634.481, 156...
138637	100121072	CHRISTCHURCH NORTHERN MOTORWAY	N	CHRISTCHURCH NORTHERN MOTORWAY	74	0	4	None	None	sealed	MULTILINESTRING ((1571390.312 5190959.558, 157...
138646	100121096	RUSSLEY ROAD	N	RUSSLEY ROAD	1	3071341	4	None	None	sealed	MULTILINESTRING ((1563395.550 5183811.195, 156...

	t50_fid	name_ascii	macronated	name	status	track_type	rway_use	veh_type	geometry
0	100120519	PALMERSTON NORTH - GISBORNE LINE	N	PALMERSTON NORTH - GISBORNE LINE	disused	single	None	None	LINESTRING (2026018.548 5694000.000, 2026005.7...
1	7927728	None	N	None	disused	single	None	None	LINESTRING (1845787.041 5784240.726, 1845749.1...
2	2462744	PALMERSTON NORTH - GISBORNE LINE	N	PALMERSTON NORTH - GISBORNE LINE	disused	single	None	None	LINESTRING (1972000.000 5666908.227, 1971949.1...
3	2462838	TANEATUA BRANCH	Y	TĀNEATUA BRANCH	disused	single	None	None	LINESTRING (1948000.000 5782634.539, 1947964.5...
4	6225482	KINGSTON BRANCH	N	KINGSTON BRANCH	disused	single	None	None	LINESTRING (1261528.423 4961310.584, 1261546.2...
...	...	...	...	...	...	...	...	...	...
527	100118890	STRATFORD - OKAHUKURA LINE	N	STRATFORD - OKAHUKURA LINE	None	single	None	None	LINESTRING (1732000.000 5657175.250, 1732026.9...
528	100118892	NORTH ISLAND MAIN TRUNK	N	NORTH ISLAND MAIN TRUNK	None	single	None	None	LINESTRING (1794254.935 5696679.903, 1794254.8...
529	100121048	STILLWATER - NGAKAWAU LINE	Y	STILLWATER - NGĀKAWAU LINE	None	single	None	None	LINESTRING (1492000.000 5319908.176, 1491999.4...
530	100121049	STILLWATER - NGAKAWAU LINE	Y	STILLWATER - NGĀKAWAU LINE	None	single	None	None	LINESTRING (1484276.055 5375189.782, 1484283.2...
531	100121050	MURUPARA LINE	N	MURUPARA LINE	None	single	None	None	LINESTRING (1932349.458 5766011.454, 1932334.6...

	Mdist_skytower_center	Mdist_skytower_north	Mdist_skytower_west	Mdist_skytower_east	Mdist_skytower_south
count	556.000000	556.000000	556.000000	556.000000	556.000000
mean	21711.173022	21741.161151	21706.032554	21707.394424	21685.279317
std	15591.755437	15569.675209	15597.048762	15597.416523	15609.189601
min	393.900000	487.300000	213.100000	401.800000	286.000000
25%	11998.775000	12189.525000	12101.775000	12018.450000	12039.750000
50%	18263.150000	18205.100000	18212.050000	18094.900000	18156.150000
75%	26795.150000	26853.250000	26910.950000	26806.200000	26840.200000
max	136328.400000	136328.400000	136328.400000	136328.400000	136328.400000

	Area_code_and_description	Area_description	Census_2006_usually_resident_population_count	Census_2013_usually_resident_population_count	Census_2018_usually_resident_population_count	Census_2006_census_night_population_count	Census_2013_census_night_population_count	Census_2018_census_night_population_count	Census_2018_Unit_record_data_source_11_2018_Census_individual_form_CURP	Census_2018_Unit_record_data_source_12_Individuals_on_the_household_listing_only_CURP	...	Census_2013_Maori_descent_04_Dont_know_CURP	Census_2013_Maori_descent_Total_stated_CURP	Census_2013_Maori_descent_99_Not_elsewhere_included_CURP	Census_2013_Maori_descent_Total_CURP	Census_2018_Maori_descent_01_Māori_descent_CURP	Census_2018_Maori_descent_02_No_Māori_descent_CURP	Census_2018_Maori_descent_04_Dont_know_CURP	Census_2018_Maori_descent_Total_stated_CURP	Census_2018_Maori_descent_99_Not_elsewhere_included_CURP	Census_2018_Maori_descent_Total_CURP
Area_code
7000000	SA1 7000000	7000000	165	144	141	177	150	138	114	6	...	3	132	12	144	135	6	3	141	0	141
7000001	SA1 7000001	7000001	93	105	114	117	108	120	87	3	...	3	87	15	105	96	18	0	114	0	114
7000002	SA1 7000002	7000002	0	0	0	0	0	0	C	C	...	C	C	C	0	C	C	C	C	C	0
7000003	SA1 7000003	7000003	216	171	225	219	168	222	171	12	...	3	144	24	171	210	15	0	225	0	225
7000004	SA1 7000004	7000004	90	102	138	90	105	129	117	3	...	0	75	27	102	102	30	3	138	0	138
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
16	16 Tasman Region	Tasman Region	44625	47157	52389	48306	51258	55206	46428	1443	...	1074	43251	3903	47157	5589	45471	1329	52389	0	52389
17	17 Nelson Region	Nelson Region	42888	46437	50880	45372	48444	53082	44922	1680	...	1173	42528	3909	46437	6336	43167	1377	50880	0	50880
18	18 Marlborough Region	Marlborough Region	42558	43416	47340	46179	46302	50562	41457	1515	...	1104	39237	4179	43416	7341	38583	1416	47340	0	47340
99	99 Area Outside Region	Area Outside Region	618	600	669	660	600	822	618	33	...	24	546	54	600	432	210	27	669	0	669
total	Total NZ (Regional Council)	Total NZ (Regional Council)	4027947	4242048	4699755	4143282	4353198	4793358	3971892	203010	...	87234	3821445	420603	4242048	869850	3715050	114855	4699755	0	4699755

	Name	Census2018_population
1000	Ormiston South	5514
982	Papatoetoe Central	5085
1059	Pukekohe West	4980
706	Aorere South	4944
919	Point England	4923
137	Weymouth North	4911
927	Mount Wellington Central	4827
974	Papatoetoe North	4779
788	Sunnyvale East	4773
128	Papatoetoe West	4755
747	Birkdale South	4725
786	Glendene North	4686
837	Clover Park North	4683
1056	Pukekohe North West	4674
749	Birkenhead West	4653
844	New Lynn South	4650
1011	Homai West	4605
796	Point Chevalier East	4602
869	Mount Roskill White Swan	4596
732	Massey South	4554

	Unnamed: 0	Loss	Zero income	$1-$5,000	$5,001-$10,000	$10,001-$15,000	$15,001-$20,000	$20,001-$25,000	$25,001-$30,000	$30,001-$35,000	...	$50,001-$60,000	$60,001-$70,000	$70,001-$100,000	$100,001-$150,000	$150,001 or More	Total stated	Not stated	Median Household\n Income ($)	Mean Household \nIncome ($)	Total
Unnamed: 0
100100	100100 North Cape	3.0	6.0	3.0	12.0	12.0	45.0	66.0	12.0	21.0	...	48.0	39.0	69.0	60.0	42.0	525.0	105.0	49400.0	65300.0	630.0
100200	100200 Rangaunu Harbour	3.0	6.0	3.0	9.0	30.0	48.0	78.0	24.0	21.0	...	42.0	63.0	123.0	96.0	48.0	693.0	81.0	56400.0	67000.0	774.0
100300	100300 Inlets Far North District	0.0	0.0	0.0	3.0	0.0	3.0	6.0	0.0	0.0	...	0.0	0.0	3.0	0.0	3.0	30.0	30.0	24400.0	41300.0	60.0
100400	100400 Karikari Peninsula	3.0	0.0	6.0	0.0	12.0	12.0	54.0	12.0	18.0	...	30.0	27.0	57.0	51.0	24.0	393.0	69.0	47200.0	62800.0	465.0
100500	100500 Tangonge	3.0	3.0	0.0	0.0	6.0	12.0	30.0	9.0	6.0	...	24.0	24.0	54.0	69.0	36.0	330.0	57.0	67100.0	81600.0	390.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
400013	400013 Snares Islands	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0
400014	400014 Oceanic Antipodes Islands	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0
400015	400015 Antipodes Islands	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0
400016	400016 Ross Dependency	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.0
Total	Total New Zealand	4974.0	10152.0	15312.0	14538.0	32031.0	59175.0	105777.0	45423.0	45849.0	...	107058.0	97539.0	242262.0	295119.0	272208.0	1526961.0	126831.0	75700.0	91300.0	1653792.0

	Mdist_skytower_center	Mdist_skytower_north	Mdist_skytower_west	Mdist_skytower_east	Mdist_skytower_south
count	409.000000	409.000000	409.000000	409.000000	409.000000
mean	22795.600244	22781.618093	22788.159902	22784.258191	22748.530073
std	16379.256603	16359.032083	16357.011292	16376.317853	16403.724191
min	557.400000	715.700000	707.700000	1143.700000	446.000000
25%	12472.900000	12402.700000	12501.100000	12532.200000	12441.400000
50%	18613.100000	18513.300000	18539.800000	18517.400000	18512.800000
75%	27316.300000	27355.900000	27221.000000	27385.600000	27368.200000
max	89091.100000	89018.800000	89160.200000	87888.800000	89163.300000

	Area_Code_and_Description	Code	Description	2001_Census_census_usually_resident_population_count(1)	2006_Census_census_usually_resident_population_count(1)	2013_Census_census_usually_resident_population_count(1)	2001_Census_census_night_population_count(2)	2006_Census_census_night_population_count(2)	2013_Census_census_night_population_count(2)	2001_Census_sex_for_the_census_usually_resident_population_count(1)_Male	...	2006_Census_Maori_descent_for_the_census_usually_resident_population_count(1)_Don't_Know	2006_Census_Maori_descent_for_the_census_usually_resident_population_count(1)_Total_people_stated	2006_Census_Maori_descent_for_the_census_usually_resident_population_count(1)_Not_Elsewhere_Included(14)	2006_Census_Maori_descent_for_the_census_usually_resident_population_count(1)_Total_people	2013_Census_Maori_descent_for_the_census_usually_resident_population_count(1)_Maori_Descent	2013_Census_Maori_descent_for_the_census_usually_resident_population_count(1)_No_Maori_Descent	2013_Census_Maori_descent_for_the_census_usually_resident_population_count(1)_Don't_Know	2013_Census_Maori_descent_for_the_census_usually_resident_population_count(1)_Total_people_stated	2013_Census_Maori_descent_for_the_census_usually_resident_population_count(1)_Not_Elsewhere_Included(14)	2013_Census_Maori_descent_for_the_census_usually_resident_population_count(1)_Total_people
0	MB 0000100	0000100	NaN	9.0	3.0	3.0	15.0	9.0	9.0	6	...	..C	..C	..C	3.0	..C	..C	..C	..C	..C	3.0
1	MB 0000200	0000200	NaN	90.0	87.0	84.0	99.0	96.0	84.0	42	...	0	81	6	90.0	69	3	3	75	9	84.0
2	MB 0000300	0000300	NaN	90.0	72.0	57.0	93.0	72.0	54.0	42	...	0	66	6	75.0	54	0	0	57	3	57.0
3	MB 0000400	0000400	NaN	30.0	21.0	30.0	45.0	39.0	30.0	18	...	0	21	3	21.0	12	6	3	21	6	27.0
4	MB 0000501	0000501	NaN	0.0	0.0	0.0	0.0	0.0	0.0	..C	...	..C	..C	..C	0.0	..C	..C	..C	..C	..C	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
49028	Footnotes for specific variables or categories	13	Consists of people who were too young to talk ...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
49029	Footnotes for specific variables or categories	14	Consists of response unidentifiable and not st...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
49030	Symbols	..C	confidential	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
49031	Symbols	*	not able to be calculated	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
49032	Source	Statistics New Zealand	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	Code	Name	AREA_SQ_KM	Area	Shape_Length	geometry	Centroid_lon	Centroid_lat	Centroid_local_area	Hdist_skytower	...	LL_area	FU_area	HGI_area	Business_area	Rural_area	Open_area	Coast_indicator	Census2013_population	Census2013_dwellings	Census2013_med_HH_income
30	505604	Kumeu East	13.475480	13.310426	24949.702400	MULTIPOLYGON (((1741052.061 5931333.839, 17409...	174.575870	-36.770909	Rodney-Kumeu-Riverhead	16136.289703	...	0.000000e+00	1.548950e+06	0.00000	142889.422393	1.037018e+07	216410.264282	True	1449.0	474.0	87700.0
31	505605	Kumeu West	24.388271	24.388271	32928.601801	MULTIPOLYGON (((1734788.301 5932715.055, 17348...	174.524933	-36.774538	Rodney-Kumeu-Riverhead	18120.060194	...	0.000000e+00	5.636526e+06	0.00000	0.000000	1.749381e+07	228438.588353	False	1725.0	609.0	95300.0
32	505804	Hatfields Beach	0.908867	0.908867	5395.241593	MULTIPOLYGON (((1751816.238 5952425.382, 17518...	174.690104	-36.569595	Hibiscus Coast	30962.434695	...	5.866738e+04	5.548376e+04	0.00000	0.000000	3.031425e+00	239098.186254	True	1380.0	492.0	74100.0
33	505805	Orewa	5.572505	4.229676	19755.382361	MULTIPOLYGON (((1750518.569 5950940.087, 17505...	174.686690	-36.589013	Hibiscus Coast	27831.510696	...	0.000000e+00	5.580409e+01	0.00000	198410.958707	1.820051e+01	667506.890608	True	8520.0	3843.0	47600.0
34	505902	Manly	3.188420	3.188420	12908.971485	MULTIPOLYGON (((1756245.400 5945964.220, 17562...	174.759445	-36.630419	Hibiscus Coast	22916.935706	...	1.725715e+04	0.000000e+00	0.00000	9822.350907	0.000000e+00	373513.517145	True	6552.0	2535.0	63800.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
967	525922	Bledisloe Park	9.643551	9.643551	16858.446790	MULTIPOLYGON (((1770838.870 5881130.225, 17709...	174.915328	-37.215688	Franklin-Pukekohe	41041.238084	...	0.000000e+00	3.229772e+06	0.00000	796129.574379	1.976364e+06	280880.995489	False	2934.0	1092.0	70100.0
968	526103	Waiuku West	2.198567	2.198567	9682.515485	MULTIPOLYGON (((1753228.371 5877597.817, 17532...	174.724591	-37.248157	Franklin-Waiuku	42771.770108	...	2.023937e+04	0.000000e+00	0.00000	208423.241402	2.600376e+05	265772.078309	True	3102.0	1221.0	56900.0
969	526104	Waiuku East	3.200239	3.158494	9009.196620	MULTIPOLYGON (((1753749.548 5877735.141, 17537...	174.738476	-37.247818	Franklin-Waiuku	42896.783550	...	7.459611e+05	0.000000e+00	0.00000	175045.781248	0.000000e+00	531138.555183	True	3498.0	1248.0	60500.0
970	526105	South Waiuku	2.247383	2.247383	9084.157884	MULTIPOLYGON (((1755346.188 5875198.493, 17551...	174.739741	-37.259971	Franklin-Waiuku	44558.447057	...	1.185644e+06	0.000000e+00	0.00000	0.000000	5.934895e+05	7326.469987	False	1599.0	546.0	85600.0
1962	616300	Browns Island	0.595336	0.595336	3430.690993	MULTIPOLYGON (((1769155.864 5922005.512, 17691...	174.894441	-36.830617	Waiheke	11500.202550	...	0.000000e+00	0.000000e+00	586560.33176	0.000000	0.000000e+00	0.000000	True	0.0	0.0	NaN